Analazing the impact of Satisfaction with financial situation of household effect Feeling of happiness¶
Introduction:¶
Happiness is a fundamental aspect of human well-being, and understanding the factors that influence it has long been a subject of study. Among these factors, financial satisfaction plays a significant role, as it impacts an individual’s ability to meet their needs and pursue personal goals. This project explores the relationship between satisfaction with one’s financial situation and feelings of happiness. By analyzing survey data, this study aims to uncover patterns and insights that highlight the importance of financial well-being in shaping happiness.
Project Overview¶
This project aims to analyze the relationship between household satisfaction with their financial situation and their feelings of happiness. Using a dataset derived from Moroccan households, the study applies various data manipulation, visualization, and geospatial analysis techniques to uncover trends and patterns.
Key questions addressed include:
- Does financial satisfaction significantly influence happiness?
- Are there differences in the relationship between financial satisfaction and happiness in different regions of Morocco?
(Dependent variable is Y = feelings of happiness, Independent variable X = atisfaction with financial situation of household)
Methodology¶
The study employs a structured approach, using Python for data manipulation, statistical analysis, and visualization. The methodology includes:
- Data Preparation: Cleaning and preprocessing the Moroccan dataset to ensure consistency and completeness.
- Exploratory Data Analysis (EDA): Generating descriptive statistics and visualizations to summarize the dataset and identify initial trends.
- Correlation and Regression Analysis: Examining the statistical relationship between financial satisfaction and happiness.
- Geospatial Analysis: Utilizing GeoPandas to map regional variations in the findings.
- Data Visualization: Leveraging Seaborn, Matplotlib, and Plotly for clear and interactive data presentation.
Key Steps¶
Data Loading and Cleaning:
- Load and view the dataset using
pandas,pyreadstatandData Wrangler (vscode extension). - Handle missing or inconsistent values to prepare for analysis.
- Load and view the dataset using
Exploratory Data Analysis (EDA):
- Use
pandas,numpyandstatsmodelsfor descriptive statistics. - Visualize data distributions and relationships using
matplotlib,seabornplotly.
- Use
Correlation and Statistical Modeling:
- Compute correlation coefficients between financial satisfaction and happiness.
- Build regression models to quantify the relationship.
Geospatial Analysis:
- Use
geopandasto integrate geographic data. - Create visualizations that highlight regional differences across Morocco.
- Use
Data Visualization and Insights:
- Design static and interactive plots using Plotly to present results.
- Interpret the findings in the context of Morocco’s socioeconomic landscape.
# Libraries used
#Manipulation/Calucaltion
import pandas as pd
import pyreadstat
import numpy as np
from statsmodels.miscmodels.ordinal_model import OrderedModel
#plotting
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
from plotly.subplots import make_subplots
import plotly.io as pio
pio.renderers.default = "notebook"
#goefileloade/read
import geopandas as gpd
import json
# pandast option setup
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 15)
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
# Loading the main dataset: df
df, meta = pyreadstat.read_sav('Project_Dataset.sav')
df
| version | doi | A_YEAR | B_COUNTRY | B_COUNTRY_ALPHA | C_COW_NUM | C_COW_ALPHA | D_INTERVIEW | J_INTDATE | FW_START | FW_END | K_TIME_START | K_TIME_END | K_DURATION | Q_MODE | N_REGION_ISO | N_REGION_WVS | N_TOWN | G_TOWNSIZE | G_TOWNSIZE2 | H_SETTLEMENT | H_URBRURAL | I_PSU | O1_LONGITUDE | O2_LATITUDE | S_INTLANGUAGE | LNGE_ISO | E_RESPINT | F_INTPRIVACY | W_WEIGHT | S018 | PWGHT | Q1 | Q2 | Q3 | Q4 | Q5 | Q6 | Q7 | Q8 | Q9 | Q10 | Q11 | Q12 | Q13 | Q14 | Q15 | Q16 | Q17 | Q18 | Q19 | Q20 | Q21 | Q22 | Q23 | Q24 | Q25 | Q26 | Q27 | Q28 | Q29 | Q30 | Q31 | Q32 | Q33 | Q33_3 | Q34 | Q34_3 | Q35 | Q35_3 | Q37 | Q38 | Q39 | Q40 | Q41 | Q42 | Q43 | Q44 | Q45 | Q46 | Q47 | Q48 | Q49 | Q50 | Q51 | Q52 | Q53 | Q54 | Q55 | Q56 | Q57 | Q58 | Q59 | Q60 | Q61 | Q62 | Q63 | Q64 | Q65 | Q66 | Q67 | Q68 | Q69 | Q70 | Q71 | Q72 | Q73 | Q74 | Q75 | Q76 | Q77 | Q78 | Q79 | Q80 | Q81 | Q82 | Q82_ARABLEAGUE | Q82_GULFCOOP | Q82_ISLCOOP | Q83 | Q84 | Q85 | Q86 | Q87 | Q88 | Q89 | Q90 | Q91 | Q92 | Q93 | Q94 | Q95 | Q96 | Q97 | Q98 | Q99 | Q100 | Q101 | Q102 | Q103 | Q104 | Q105 | Q106 | Q107 | Q108 | Q109 | Q110 | Q111 | Q112 | Q113 | Q114 | Q115 | Q116 | Q117 | Q118 | Q119 | Q120 | Q121 | Q122 | Q123 | Q124 | Q125 | Q126 | Q127 | Q128 | Q129 | Q130 | Q131 | Q132 | Q133 | Q134 | Q135 | Q136 | Q137 | Q138 | Q139 | Q140 | Q141 | Q142 | Q143 | Q144 | Q145 | Q146 | Q147 | Q148 | Q149 | Q150 | Q151 | Q152 | Q153 | Q154 | Q155 | Q156 | Q157 | Q158 | Q159 | Q160 | Q161 | Q162 | Q163 | Q164 | Q165 | Q166 | Q167 | Q168 | Q169 | Q170 | Q171 | Q172 | Q173 | Q174 | Q175 | Q176 | Q177 | Q178 | Q179 | Q180 | Q181 | Q182 | Q183 | Q184 | Q185 | Q186 | Q187 | Q188 | Q189 | Q190 | Q191 | Q192 | Q193 | Q194 | Q195 | Q196 | Q197 | Q198 | Q199 | Q200 | Q201 | Q202 | Q203 | Q204 | Q205 | Q206 | Q207 | Q208 | Q209 | Q210 | Q211 | Q212 | Q213 | Q214 | Q215 | Q216 | Q217 | Q218 | Q219 | Q220 | Q221 | Q222 | Q223 | Q223_ABREV | Q223_LOCAL | Q224 | Q225 | Q226 | Q227 | Q228 | Q229 | Q230 | Q231 | Q232 | Q233 | Q234 | Q234A | Q235 | Q236 | Q237 | Q238 | Q239 | Q240 | Q241 | Q242 | Q243 | Q244 | Q245 | Q246 | Q247 | Q248 | Q249 | Q250 | Q251 | Q252 | Q253 | Q254 | Q255 | Q256 | Q257 | Q258 | Q259 | Q260 | Q261 | Q262 | X003R | X003R2 | Q263 | Q264 | Q265 | Q266 | Q267 | Q268 | Q269 | Q270 | Q271 | Q272 | Q273 | Q274 | Q275 | Q275R | Q276 | Q276R | Q277 | Q277R | Q278 | Q278R | Q279 | Q280 | Q281 | Q282 | Q283 | Q284 | Q285 | Q286 | Q287 | Q288 | Q288R | Q289 | Q289CS9 | Q290 | Q291G1 | Q291G2 | Q291G3 | Q291G4 | Q291G5 | Q291G6 | Q291P1 | Q291P2 | Q291P3 | Q291P4 | Q291P5 | Q291P6 | Q291UN1 | Q291UN2 | Q291UN3 | Q291UN4 | Q291UN5 | Q291UN6 | Q292A | Q292B | Q292C | Q292D | Q292E | Q292F | Q292G | Q292H | Q292I | Q292J | Q292K | Q292L | Q292M | Q292N | Q292O | Q293 | Q294A | Q294B | Y001 | Y002 | Y003 | SACSECVAL | SACSECVALB | RESEMAVAL | RESEMAVALB | I_AUTHORITY | I_NATIONALISM | I_DEVOUT | DEFIANCE | I_RELIGIMP | I_RELIGBEL | I_RELIGPRAC | DISBELIEF | I_NORM1 | I_NORM2 | I_NORM3 | RELATIVISM | I_TRUSTARMY | I_TRUSTPOLICE | I_TRUSTCOURTS | SCEPTICISM | I_INDEP | I_IMAGIN | I_NONOBED | AUTONOMY | I_WOMJOB | I_WOMPOL | I_WOMEDU | EQUALITY | I_HOMOLIB | I_ABORTLIB | I_DIVORLIB | CHOICE | I_VOICE1 | I_VOICE2 | I_VOI2_00 | VOICE | SECVALWGT | WEIGHT1A | WEIGHT1B | WEIGHT2A | WEIGHT2B | WEIGHT3A | WEIGHT3B | WEIGHT4A | WEIGHT4B | RESEMAVALBWGT | RESEMAVALWGT | SECVALBWGT | Y001_1 | Y001_2 | Y001_3 | Y001_4 | Y001_5 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.0 | 0.0 | 2021.0 | 504.0 | MAR | 600.0 | MOR | 504070001.0 | 20211112.0 | 202111.0 | 202112.0 | 10.05 | 10.43 | 38.0 | 2.0 | 504009.0 | 504019.0 | 504099.0 | 1.0 | 1.0 | 4.0 | 2.0 | 99.0 | -8.84 | 29.49 | 170.0 | ar | 1.0 | 2.0 | 1.0 | 0.833333 | 31083.33333 | 1.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1.0 | 1.0 | 2.0 | 2.0 | 2.0 | 2.0 | 1.0 | 2.0 | 1.0 | 1.0 | 2.0 | 2.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 3.0 | 1.0 | 1.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1.0 | 1.0 | 1.0 | 3.0 | 2.0 | 2.0 | 1.0 | 3.0 | 2.0 | 3.0 | 5.0 | 8.0 | 6.0 | 4.0 | 3.0 | 4.0 | 4.0 | 4.0 | 3.0 | 2.0 | 1.0 | 3.0 | 2.0 | 4.0 | 4.0 | 2.0 | 2.0 | 3.0 | 3.0 | 2.0 | 3.0 | 3.0 | 1.0 | 4.0 | 4.0 | 4.0 | 3.0 | 2.0 | 1.0 | 1.0 | 3.0 | 1.0 | 2.0 | 3.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 3.0 | 2.0 | 3.0 | 4.0 | 4.0 | 2.0 | 5.0 | 3.0 | 3.0 | 2.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6.0 | 4.0 | 5.0 | 5.0 | 5.0 | 2.0 | 10.0 | 3.0 | 3.0 | 2.0 | 2.0 | 3.0 | 3.0 | 3.0 | 8.0 | 3.0 | 2.0 | 2.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 0.0 | 3.0 | 2.0 | 3.0 | 3.0 | 2.0 | 2.0 | 3.0 | 3.0 | 3.0 | 1.0 | 2.0 | 1.0 | 2.0 | 2.0 | 2.0 | 2.0 | 1.0 | 1.0 | 2.0 | 2.0 | 2.0 | 2.0 | 1.0 | 3.0 | 1.0 | 4.0 | 2.0 | 4.0 | 6.0 | 8.0 | 8.0 | 5.0 | 6.0 | 8.0 | 10.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 2.0 | 1.0 | 4.0 | 5.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 7.0 | 2.0 | 1.0 | 6.0 | 2.0 | 8.0 | 1.0 | 1.0 | 1.0 | 3.0 | 10.0 | 1.0 | 4.0 | 4.0 | 2.0 | 3.0 | 5.0 | 1.0 | 2.0 | 2.0 | 4.0 | 2.0 | 2.0 | 5.0 | 1.0 | 1.0 | 3.0 | 3.0 | 2.0 | 3.0 | 2.0 | 3.0 | 2.0 | 3.0 | 3.0 | 3.0 | 2.0 | 2.0 | 504011.0 | 504011.0 | 504011.0 | 1.0 | 3.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 4.0 | 4.0 | 3.0 | 2.0 | 5.0 | 4.0 | 4.0 | 4.0 | 2.0 | 1.0 | 8.0 | 10.0 | 7.0 | 6.0 | 9.0 | 4.0 | 8.0 | 9.0 | 5.0 | 10.0 | 10.0 | 3.0 | 1.0 | 3.0 | 3.0 | 4.0 | 4.0 | 4.0 | 3.0 | 4.0 | 2.0 | 1969.0 | 52.0 | 4.0 | 3.0 | 1.0 | 1.0 | 1.0 | 504.0 | 504.0 | 504.0 | 1.0 | 8.0 | 1.0 | 500.0 | 1.0 | 4.0 | 1.0 | 1.0 | 0.0 | 1.0 | 0.0 | 1.0 | 0.0 | 1.0 | 1.0 | 5.0 | 5.0 | 9.0 | 9.0 | 2.0 | 1.0 | 3.0 | 2.0 | 6.0 | 2.0 | 5.0 | 50000000.0 | 504005.0 | 5.0 | 2.0 | 5.0 | 4.0 | 5.0 | 5.0 | 5.0 | 2.0 | 5.0 | 4.0 | 5.0 | 4.0 | 3.0 | 4.0 | 4.0 | 2.0 | 5.0 | 3.0 | 5.0 | 4.0 | 3.0 | 2.0 | 2.0 | 4.0 | 2.0 | 2.0 | 1.0 | 1.0 | 1.0 | 5.0 | 4.0 | 1.0 | 3.0 | 1.0 | 1.0 | 1.0 | 3.0 | 2.0 | -2.0 | 0.331667 | 0.443333 | 0.235139 | 0.262778 | 1.0 | 0.66 | 0.00 | 0.553333 | 0.00 | 1.0 | 0.000000 | 0.333333 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.66 | 0.66 | 0.00 | 0.440000 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.25 | 0.00 | 0.66 | 0.303333 | 0.000000 | 0.000000 | 0.666667 | 0.222222 | 0.33 | 0.5 | 0.415 | 0.415 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 1.0 | 1.0 | 0.0 |
| 1 | 0.0 | 0.0 | 2021.0 | 504.0 | MAR | 600.0 | MOR | 504070002.0 | 20211112.0 | 202111.0 | 202112.0 | 10.46 | 11.19 | 33.0 | 2.0 | 504009.0 | 504019.0 | 504099.0 | 1.0 | 1.0 | 4.0 | 2.0 | 99.0 | -8.84 | 29.49 | 170.0 | ar | 1.0 | 1.0 | 1.0 | 0.833333 | 31083.33333 | 1.0 | 2.0 | 3.0 | 4.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 2.0 | 2.0 | 2.0 | 2.0 | 1.0 | 1.0 | 1.0 | 2.0 | 2.0 | 2.0 | 2.0 | 1.0 | 1.0 | 1.0 | 3.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 5.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 3.0 | 3.0 | 1.0 | 1.0 | 4.0 | 4.0 | 5.0 | 4.0 | 2.0 | 4.0 | 1.0 | 1.0 | 2.0 | 4.0 | 3.0 | 2.0 | 1.0 | 3.0 | 4.0 | 4.0 | 4.0 | 3.0 | 1.0 | 1.0 | 4.0 | 4.0 | 3.0 | 1.0 | 3.0 | 4.0 | 4.0 | 4.0 | 3.0 | 3.0 | 4.0 | 4.0 | 4.0 | 2.0 | 2.0 | 1.0 | 3.0 | 3.0 | 3.0 | 2.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 5.0 | 3.0 | 2.0 | 2.0 | 2.0 | 0.0 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 10.0 | 10.0 | 9.0 | 6.0 | 6.0 | 1.0 | 10.0 | 3.0 | 4.0 | 3.0 | 1.0 | 4.0 | 4.0 | 0.0 | 10.0 | 1.0 | 2.0 | 2.0 | 0.0 | 0.0 | 2.0 | 2.0 | 2.0 | 2.0 | 4.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1.0 | 1.0 | 4.0 | 1.0 | 3.0 | 2.0 | 3.0 | 10.0 | 10.0 | 10.0 | 10.0 | 8.0 | 1.0 | 1.0 | 1.0 | 2.0 | 2.0 | 2.0 | 4.0 | 4.0 | 5.0 | 5.0 | 1.0 | 2.0 | 2.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 8.0 | 5.0 | 5.0 | 5.0 | 5.0 | 9.0 | 5.0 | 5.0 | 5.0 | 5.0 | 6.0 | 1.0 | 4.0 | 4.0 | 2.0 | 2.0 | 4.0 | 4.0 | 5.0 | 1.0 | 5.0 | 1.0 | 1.0 | 4.0 | 2.0 | 1.0 | 2.0 | 2.0 | 1.0 | 2.0 | 2.0 | 3.0 | 2.0 | 2.0 | 3.0 | 3.0 | 3.0 | 3.0 | 504009.0 | 504009.0 | 504009.0 | 4.0 | 3.0 | 4.0 | 4.0 | 4.0 | 4.0 | 1.0 | 1.0 | 3.0 | 2.0 | 1.0 | 5.0 | 1.0 | 1.0 | 4.0 | 1.0 | 1.0 | 1.0 | 10.0 | 10.0 | 10.0 | 10.0 | 10.0 | 10.0 | 10.0 | 10.0 | 5.0 | 8.0 | 6.0 | 7.0 | 3.0 | 1.0 | 1.0 | 1.0 | 2.0 | 3.0 | 4.0 | 2.0 | 1998.0 | 23.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 504.0 | 504.0 | 504.0 | 1.0 | 6.0 | 2.0 | 500.0 | 6.0 | 0.0 | 6.0 | 3.0 | 3.0 | 2.0 | 0.0 | 1.0 | 0.0 | 1.0 | 1.0 | NaN | 4.0 | NaN | 6.0 | 3.0 | 2.0 | 2.0 | 4.0 | 7.0 | 2.0 | 5.0 | 50000000.0 | 504005.0 | 5.0 | 1.0 | 5.0 | 5.0 | 5.0 | 5.0 | 1.0 | 1.0 | 1.0 | 5.0 | 5.0 | 5.0 | 1.0 | 5.0 | 3.0 | 3.0 | 3.0 | 2.0 | 5.0 | 5.0 | 1.0 | 1.0 | 5.0 | 4.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 5.0 | 1.0 | 1.0 | 0.0 | NaN | NaN | 2.0 | 1.0 | -1.0 | 0.360556 | 0.111111 | 0.360556 | 0.387778 | 0.0 | 0.00 | 0.00 | 0.000000 | 0.00 | 0.0 | 0.666667 | 0.222222 | 1.0 | 1.0 | 1.0 | 1.000000 | 0.00 | 0.00 | 0.66 | 0.220000 | 0.0 | 1.0 | 1.0 | 0.666667 | 0.00 | 0.00 | 0.66 | 0.220000 | 0.444444 | 0.444444 | 0.777778 | 0.555556 | 0.00 | 0.0 | 0.000 | 0.000 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 |
| 2 | 0.0 | 0.0 | 2021.0 | 504.0 | MAR | 600.0 | MOR | 504070003.0 | 20211112.0 | 202111.0 | 202112.0 | 11.28 | 12.09 | 41.0 | 2.0 | 504009.0 | 504019.0 | 504099.0 | 1.0 | 1.0 | 4.0 | 2.0 | 99.0 | -8.84 | 29.49 | 170.0 | ar | 1.0 | 2.0 | 1.0 | 0.833333 | 31083.33333 | 1.0 | 2.0 | 1.0 | 4.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 1.0 | 2.0 | 2.0 | 1.0 | 2.0 | 2.0 | 2.0 | 1.0 | 2.0 | 1.0 | 2.0 | 2.0 | 1.0 | 1.0 | 1.0 | 3.0 | 2.0 | 3.0 | 3.0 | 3.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 3.0 | 1.0 | 1.0 | 3.0 | 4.0 | 2.0 | 2.0 | 3.0 | 3.0 | 3.0 | 2.0 | 1.0 | 1.0 | 2.0 | 2.0 | 1.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 2.0 | 1.0 | 2.0 | 3.0 | 4.0 | 1.0 | 1.0 | 3.0 | 3.0 | 4.0 | 1.0 | 2.0 | 4.0 | 1.0 | 1.0 | 1.0 | 3.0 | 3.0 | 4.0 | 4.0 | 4.0 | 4.0 | 3.0 | 4.0 | 4.0 | 4.0 | 1.0 | 2.0 | 1.0 | 9.0 | 3.0 | 3.0 | 2.0 | 0.0 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 10.0 | 2.0 | 3.0 | 2.0 | 2.0 | 3.0 | 4.0 | 4.0 | 10.0 | 4.0 | 0.0 | 2.0 | 2.0 | 2.0 | 0.0 | 2.0 | 2.0 | 0.0 | 2.0 | 2.0 | 3.0 | 4.0 | 4.0 | 2.0 | 3.0 | 3.0 | 1.0 | 1.0 | 1.0 | 2.0 | 4.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1.0 | 4.0 | 2.0 | 3.0 | 8.0 | 8.0 | 7.0 | 5.0 | 2.0 | 7.0 | 10.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 1.0 | 4.0 | 7.0 | 2.0 | 2.0 | 2.0 | 1.0 | 1.0 | 1.0 | 4.0 | 7.0 | 2.0 | 1.0 | 1.0 | 5.0 | 3.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 3.0 | 3.0 | 4.0 | 3.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 2.0 | 2.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 2.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 4.0 | 4.0 | 4.0 | 1.0 | 4.0 | 2.0 | 3.0 | 3.0 | 1.0 | 2.0 | 3.0 | 3.0 | 1.0 | 3.0 | 4.0 | 1.0 | 1.0 | 3.0 | 2.0 | 2.0 | 8.0 | 10.0 | 7.0 | 10.0 | 10.0 | 5.0 | 10.0 | 6.0 | 8.0 | 10.0 | 10.0 | 6.0 | 6.0 | 2.0 | 2.0 | 1.0 | 2.0 | 4.0 | 4.0 | 4.0 | 1.0 | 1986.0 | 35.0 | 3.0 | 2.0 | 1.0 | 1.0 | 1.0 | 504.0 | 504.0 | 504.0 | 1.0 | 4.0 | 2.0 | 170.0 | 3.0 | 1.0 | 6.0 | 3.0 | 4.0 | 2.0 | 6.0 | 3.0 | 6.0 | 3.0 | 7.0 | NaN | 9.0 | 9.0 | 9.0 | 3.0 | 1.0 | 2.0 | 2.0 | 7.0 | 2.0 | 5.0 | 50000000.0 | 504005.0 | 5.0 | 4.0 | 1.0 | 4.0 | 4.0 | 1.0 | 5.0 | 5.0 | 2.0 | 4.0 | 4.0 | 2.0 | 2.0 | 3.0 | 2.0 | 3.0 | 2.0 | 1.0 | 5.0 | 5.0 | 3.0 | 2.0 | 4.0 | 5.0 | 1.0 | 3.0 | 5.0 | 4.0 | 1.0 | 4.0 | 4.0 | 4.0 | 2.0 | 7.0 | NaN | NaN | 3.0 | 2.0 | 0.0 | 0.291389 | 0.249444 | 0.304583 | 0.360000 | 0.0 | 0.33 | 0.00 | 0.110000 | 0.00 | 1.0 | 0.166667 | 0.388889 | 1.0 | 1.0 | 0.0 | 0.666667 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.0 | 0.0 | 1.0 | 0.333333 | 0.50 | 0.00 | 0.66 | 0.386667 | 0.000000 | 0.333333 | 0.666667 | 0.333333 | 0.33 | 0.0 | 0.165 | 0.165 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 1.0 | 1.0 | 1.0 |
| 3 | 0.0 | 0.0 | 2021.0 | 504.0 | MAR | 600.0 | MOR | 504070004.0 | 20211112.0 | 202111.0 | 202112.0 | 12.08 | 12.42 | 34.0 | 2.0 | 504009.0 | 504019.0 | 504099.0 | 1.0 | 1.0 | 4.0 | 2.0 | 99.0 | -8.84 | 29.49 | 170.0 | ar | 1.0 | 1.0 | 1.0 | 0.833333 | 31083.33333 | 1.0 | 1.0 | 4.0 | 2.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1.0 | 2.0 | 2.0 | 1.0 | 2.0 | 2.0 | 2.0 | 1.0 | 1.0 | 2.0 | 2.0 | 2.0 | 2.0 | 1.0 | 2.0 | 2.0 | 2.0 | 2.0 | 1.0 | 1.0 | 1.0 | 3.0 | 3.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 3.0 | 3.0 | 1.0 | 1.0 | 2.0 | 1.0 | 8.0 | 1.0 | 6.0 | 4.0 | 3.0 | 4.0 | 4.0 | 4.0 | 3.0 | 2.0 | 1.0 | 3.0 | 3.0 | 4.0 | 3.0 | 3.0 | 3.0 | 1.0 | 4.0 | 4.0 | 4.0 | 1.0 | 1.0 | 3.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 4.0 | 4.0 | 3.0 | 4.0 | 4.0 | 4.0 | 10.0 | 3.0 | 3.0 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 10.0 | 10.0 | 10.0 | 10.0 | 10.0 | 2.0 | 7.0 | 2.0 | 3.0 | 2.0 | 2.0 | 3.0 | 3.0 | 1.0 | 10.0 | 4.0 | 2.0 | 0.0 | 1.0 | 1.0 | 0.0 | 0.0 | 2.0 | 0.0 | 3.0 | 1.0 | 3.0 | 2.0 | 2.0 | 4.0 | 2.0 | 3.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 2.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1.0 | 1.0 | 1.0 | 4.0 | 1.0 | 3.0 | 1.0 | 3.0 | 10.0 | 10.0 | 10.0 | 8.0 | 1.0 | 10.0 | 10.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 2.0 | 1.0 | 5.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 4.0 | 4.0 | 2.0 | 2.0 | 4.0 | 1.0 | 4.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 3.0 | 1.0 | 3.0 | 3.0 | 1.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 2.0 | 4.0 | 4.0 | 4.0 | 3.0 | 3.0 | 1.0 | 1.0 | 3.0 | 2.0 | 2.0 | 2.0 | 1.0 | 2.0 | 1.0 | 3.0 | 1.0 | 4.0 | 4.0 | 4.0 | 3.0 | 5.0 | 10.0 | 10.0 | 10.0 | 10.0 | 1.0 | 10.0 | 10.0 | 1.0 | 1.0 | 10.0 | 6.0 | 6.0 | 1.0 | 1.0 | 2.0 | 4.0 | 4.0 | 4.0 | 4.0 | 1.0 | 1993.0 | 28.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 504.0 | 504.0 | 504.0 | 1.0 | 4.0 | 2.0 | 170.0 | 1.0 | 2.0 | 1.0 | 1.0 | 0.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 7.0 | 1.0 | 3.0 | 0.0 | 9.0 | 2.0 | 2.0 | 2.0 | 3.0 | 6.0 | 2.0 | 5.0 | 50000000.0 | 504005.0 | 2.0 | 1.0 | 1.0 | 2.0 | 5.0 | 3.0 | 4.0 | 1.0 | 1.0 | 2.0 | 5.0 | 4.0 | 1.0 | 1.0 | 1.0 | 3.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 2.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 5.0 | NaN | NaN | 1.0 | 1.0 | -1.0 | 0.000000 | 0.000000 | 0.055000 | 0.110000 | 0.0 | 0.00 | 0.00 | 0.000000 | 0.00 | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.00 | 0.00 | 0.66 | 0.220000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.0 | 0.000 | 0.000 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 4 | 0.0 | 0.0 | 2021.0 | 504.0 | MAR | 600.0 | MOR | 504070005.0 | 20211112.0 | 202111.0 | 202112.0 | 13.18 | 13.55 | 37.0 | 2.0 | 504009.0 | 504019.0 | 504099.0 | 1.0 | 1.0 | 4.0 | 2.0 | 99.0 | -8.84 | 29.49 | 170.0 | ar | 1.0 | 1.0 | 1.0 | 0.833333 | 31083.33333 | 1.0 | 2.0 | 3.0 | 3.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1.0 | 2.0 | 2.0 | 2.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 2.0 | 2.0 | 1.0 | 1.0 | 3.0 | 3.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 3.0 | 3.0 | 3.0 | 2.0 | 4.0 | 4.0 | 3.0 | 1.0 | 3.0 | 2.0 | 2.0 | 3.0 | 2.0 | 7.0 | 7.0 | 4.0 | 2.0 | 3.0 | 2.0 | 4.0 | 4.0 | 3.0 | 2.0 | 1.0 | 3.0 | 2.0 | 3.0 | 3.0 | 3.0 | 2.0 | 2.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 4.0 | 3.0 | 4.0 | 4.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 5.0 | 2.0 | 1.0 | 3.0 | 1.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 6.0 | 5.0 | 3.0 | 1.0 | 5.0 | 1.0 | 8.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 4.0 | 10.0 | 3.0 | 2.0 | 1.0 | 1.0 | 1.0 | 0.0 | 2.0 | 0.0 | 1.0 | 2.0 | 1.0 | 2.0 | 2.0 | 4.0 | 2.0 | 2.0 | 2.0 | 1.0 | 1.0 | 2.0 | 2.0 | 2.0 | 1.0 | 2.0 | 2.0 | 1.0 | 1.0 | 1.0 | 2.0 | 2.0 | 2.0 | 1.0 | 2.0 | 1.0 | 3.0 | 1.0 | 2.0 | 8.0 | 10.0 | 7.0 | 6.0 | 1.0 | 8.0 | 10.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1.0 | 1.0 | 7.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 3.0 | 1.0 | 4.0 | 1.0 | 8.0 | 2.0 | 1.0 | 4.0 | 5.0 | 10.0 | 1.0 | 4.0 | 4.0 | 4.0 | 3.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 1.0 | 1.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 2.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 4.0 | 4.0 | 4.0 | 1.0 | 2.0 | 4.0 | 2.0 | 2.0 | 1.0 | 2.0 | 3.0 | 2.0 | 1.0 | 3.0 | 4.0 | 4.0 | 3.0 | 4.0 | 1.0 | 1.0 | 7.0 | 1.0 | 3.0 | 5.0 | 9.0 | 6.0 | 10.0 | 10.0 | 3.0 | 9.0 | 8.0 | 5.0 | 3.0 | 3.0 | 3.0 | 2.0 | 2.0 | 3.0 | 3.0 | 4.0 | 1.0 | 1999.0 | 22.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 504.0 | 504.0 | 504.0 | 1.0 | 5.0 | 1.0 | 170.0 | 6.0 | 0.0 | 1.0 | 1.0 | NaN | NaN | 3.0 | 2.0 | 2.0 | 1.0 | 1.0 | NaN | 5.0 | NaN | 9.0 | 3.0 | 2.0 | 3.0 | 4.0 | 7.0 | 2.0 | 5.0 | 50000000.0 | 504005.0 | 2.0 | 4.0 | 1.0 | 1.0 | 4.0 | 5.0 | 4.0 | 4.0 | 2.0 | 2.0 | 4.0 | 2.0 | 4.0 | 4.0 | 1.0 | 4.0 | 4.0 | 4.0 | 5.0 | 5.0 | 2.0 | 2.0 | 4.0 | 5.0 | 1.0 | 1.0 | 5.0 | 5.0 | 1.0 | 5.0 | 5.0 | 4.0 | 5.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 0.317500 | 0.360000 | 0.276667 | 0.220000 | 0.5 | 0.66 | 0.00 | 0.386667 | 0.00 | 1.0 | 0.000000 | 0.333333 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.33 | 0.66 | 0.66 | 0.550000 | 1.0 | 0.0 | 1.0 | 0.666667 | 0.00 | 0.66 | 0.66 | 0.440000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.0 | 0.000 | 0.000 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
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| 1195 | 0.0 | 0.0 | 2021.0 | 504.0 | MAR | 600.0 | MOR | 504071196.0 | 20211213.0 | 202111.0 | 202112.0 | 14.12 | 15.11 | 59.0 | 2.0 | 504002.0 | 504012.0 | 504081.0 | 5.0 | 3.0 | 3.0 | 1.0 | 81.0 | -2.73 | 34.94 | 170.0 | ar | 1.0 | 1.0 | 1.0 | 0.833333 | 31083.33333 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 2.0 | 2.0 | 1.0 | 2.0 | 2.0 | 2.0 | 1.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 1.0 | 2.0 | 2.0 | 1.0 | 1.0 | 2.0 | 3.0 | 2.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 4.0 | 2.0 | 2.0 | 1.0 | 1.0 | 1.0 | 2.0 | 2.0 | 3.0 | 1.0 | 1.0 | 1.0 | 1.0 | 10.0 | 6.0 | 6.0 | 4.0 | 4.0 | 3.0 | 3.0 | 4.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 1.0 | 2.0 | 2.0 | 3.0 | 3.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 3.0 | 3.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 3.0 | 3.0 | 10.0 | 3.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 1.0 | 2.0 | 0.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 10.0 | 5.0 | 10.0 | 5.0 | 5.0 | 2.0 | 5.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 3.0 | 3.0 | 4.0 | 4.0 | 0.0 | 2.0 | 0.0 | 0.0 | 0.0 | 2.0 | 0.0 | 0.0 | 1.0 | 2.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 3.0 | 3.0 | 1.0 | 2.0 | 2.0 | 1.0 | 1.0 | 2.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1.0 | 3.0 | 2.0 | 2.0 | 4.0 | 2.0 | 3.0 | 9.0 | 9.0 | 9.0 | 9.0 | 4.0 | 8.0 | 10.0 | 1.0 | 1.0 | 1.0 | 1.0 | 3.0 | 2.0 | 7.0 | 3.0 | 2.0 | 2.0 | 2.0 | 4.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 6.0 | 6.0 | 6.0 | 5.0 | 5.0 | 5.0 | 5.0 | 4.0 | 4.0 | 6.0 | 5.0 | 6.0 | 2.0 | 2.0 | 2.0 | 1.0 | 1.0 | 5.0 | 2.0 | 5.0 | 1.0 | 3.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 2.0 | 1.0 | 1.0 | 1.0 | 2.0 | 2.0 | 1.0 | 1.0 | 504004.0 | 504004.0 | 504004.0 | 1.0 | 4.0 | 2.0 | 2.0 | 2.0 | 1.0 | 2.0 | 3.0 | 1.0 | 1.0 | 1.0 | 3.0 | 3.0 | 3.0 | 3.0 | 1.0 | 3.0 | 10.0 | 7.0 | 5.0 | 7.0 | 6.0 | 6.0 | 7.0 | 6.0 | 7.0 | 6.0 | 10.0 | 10.0 | 10.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 1.0 | 1980.0 | 41.0 | 3.0 | 2.0 | 1.0 | 1.0 | 1.0 | 504.0 | 504.0 | 504.0 | 1.0 | 5.0 | 1.0 | 170.0 | 6.0 | 0.0 | 4.0 | 2.0 | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | NaN | 1.0 | NaN | 6.0 | 2.0 | 1.0 | 2.0 | 3.0 | 6.0 | 2.0 | 5.0 | 50000000.0 | 504005.0 | 2.0 | 4.0 | 4.0 | 1.0 | 4.0 | 2.0 | 2.0 | 5.0 | 2.0 | 2.0 | 4.0 | 2.0 | 2.0 | 4.0 | 4.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 4.0 | 4.0 | 2.0 | 2.0 | 4.0 | 4.0 | 2.0 | 2.0 | 4.0 | 2.0 | 2.0 | 4.0 | 4.0 | 5.0 | 4.0 | 4.0 | 5.0 | 3.0 | 2.0 | 0.526667 | 0.388333 | 0.628796 | 0.424259 | 0.0 | 0.33 | 0.00 | 0.110000 | 0.00 | 1.0 | 1.000000 | 0.666667 | 1.0 | 1.0 | 1.0 | 1.000000 | 0.33 | 0.33 | 0.33 | 0.330000 | 1.0 | 0.0 | 1.0 | 0.666667 | 0.00 | 0.33 | 0.66 | 0.330000 | 0.444444 | 0.555556 | 0.555556 | 0.518519 | 1.00 | 1.0 | 1.000 | 1.000 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 1196 | 0.0 | 0.0 | 2021.0 | 504.0 | MAR | 600.0 | MOR | 504071197.0 | 20211213.0 | 202111.0 | 202112.0 | 15.30 | 16.25 | 55.0 | 2.0 | 504002.0 | 504012.0 | 504081.0 | 5.0 | 3.0 | 3.0 | 1.0 | 81.0 | -2.73 | 34.94 | 170.0 | ar | 2.0 | 1.0 | 1.0 | 0.833333 | 31083.33333 | 2.0 | 2.0 | 2.0 | 4.0 | 2.0 | 2.0 | 2.0 | 1.0 | 1.0 | 2.0 | 2.0 | 2.0 | 1.0 | 1.0 | 2.0 | 2.0 | 1.0 | 1.0 | 2.0 | 2.0 | 2.0 | 1.0 | 2.0 | 1.0 | 2.0 | 2.0 | 2.0 | 2.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 1.0 | 1.0 | 4.0 | 2.0 | 2.0 | 1.0 | 1.0 | 2.0 | 4.0 | 1.0 | 3.0 | 1.0 | 3.0 | 2.0 | 3.0 | 7.0 | 6.0 | 6.0 | 3.0 | 3.0 | 3.0 | 2.0 | 3.0 | 3.0 | 2.0 | 4.0 | 4.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 3.0 | 3.0 | 4.0 | 3.0 | 3.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 5.0 | 1.0 | 1.0 | 2.0 | 1.0 | 2.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 10.0 | 10.0 | 5.0 | 5.0 | 8.0 | 2.0 | 10.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 2.0 | 3.0 | 3.0 | 2.0 | 0.0 | 2.0 | 0.0 | 2.0 | 2.0 | 2.0 | 2.0 | 3.0 | 3.0 | 2.0 | 2.0 | 3.0 | 3.0 | 3.0 | 2.0 | 2.0 | 1.0 | 1.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 1.0 | 2.0 | 1.0 | 1.0 | 3.0 | 2.0 | 3.0 | 3.0 | 4.0 | 10.0 | 10.0 | 10.0 | 10.0 | 3.0 | 10.0 | 6.0 | 1.0 | 1.0 | 1.0 | 1.0 | 3.0 | 2.0 | 7.0 | 7.0 | 2.0 | 1.0 | 1.0 | 5.0 | 8.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 8.0 | 8.0 | 6.0 | 6.0 | 6.0 | 4.0 | 6.0 | 6.0 | 4.0 | 4.0 | 6.0 | 4.0 | 6.0 | 4.0 | 4.0 | 4.0 | 4.0 | 3.0 | 5.0 | 5.0 | 5.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 4.0 | 4.0 | 4.0 | 4.0 | 3.0 | 1.0 | 1.0 | 4.0 | 4.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 5.0 | 1.0 | 2.0 | 4.0 | 4.0 | 4.0 | 1.0 | 10.0 | 5.0 | 5.0 | 10.0 | 5.0 | 10.0 | 10.0 | 5.0 | 7.0 | 5.0 | 5.0 | 5.0 | 4.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1999.0 | 22.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 504.0 | 504.0 | 504.0 | 1.0 | 4.0 | 1.0 | 170.0 | 6.0 | 0.0 | 3.0 | 2.0 | NaN | NaN | 2.0 | 1.0 | 2.0 | 1.0 | 6.0 | NaN | 0.0 | NaN | 5.0 | 2.0 | 2.0 | 2.0 | 3.0 | 6.0 | 2.0 | 5.0 | 50000000.0 | 504005.0 | 4.0 | 2.0 | 2.0 | 4.0 | 4.0 | 4.0 | 4.0 | 2.0 | 2.0 | 4.0 | 4.0 | 4.0 | 4.0 | 2.0 | 2.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 2.0 | 2.0 | 4.0 | 4.0 | 2.0 | 4.0 | 4.0 | 4.0 | 2.0 | 4.0 | 4.0 | 2.0 | 2.0 | 0.0 | NaN | NaN | 3.0 | 2.0 | 1.0 | 0.776667 | 0.610000 | 0.518889 | 0.581111 | 1.0 | 0.00 | 0.33 | 0.443333 | 0.33 | 1.0 | 1.000000 | 0.776667 | 1.0 | 1.0 | 1.0 | 1.000000 | 0.66 | 1.00 | 1.00 | 0.886667 | 1.0 | 0.0 | 0.0 | 0.333333 | 0.50 | 0.66 | 0.66 | 0.606667 | 0.333333 | 0.777778 | 0.555556 | 0.555556 | 0.66 | 0.5 | 0.580 | 0.580 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 1.0 | 0.0 |
| 1197 | 0.0 | 0.0 | 2021.0 | 504.0 | MAR | 600.0 | MOR | 504071198.0 | 20211213.0 | 202111.0 | 202112.0 | 16.02 | 17.10 | 68.0 | 2.0 | 504002.0 | 504012.0 | 504081.0 | 5.0 | 3.0 | 3.0 | 1.0 | 81.0 | -2.73 | 34.94 | 170.0 | ar | 1.0 | 1.0 | 1.0 | 0.833333 | 31083.33333 | 1.0 | 4.0 | 2.0 | 4.0 | 2.0 | 1.0 | 1.0 | 2.0 | 2.0 | 1.0 | 2.0 | 2.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 1.0 | 2.0 | 2.0 | 1.0 | 1.0 | 2.0 | 1.0 | 1.0 | 2.0 | 1.0 | 1.0 | 3.0 | 4.0 | 3.0 | 2.0 | 2.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 4.0 | 2.0 | 2.0 | 4.0 | 4.0 | 1.0 | 3.0 | 3.0 | 1.0 | 2.0 | 3.0 | 6.0 | 10.0 | 3.0 | 4.0 | 4.0 | 2.0 | 2.0 | 4.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 3.0 | 3.0 | 1.0 | 1.0 | 3.0 | 2.0 | 4.0 | 2.0 | 2.0 | 3.0 | 3.0 | 3.0 | 2.0 | 2.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 4.0 | 3.0 | 3.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 1.0 | 2.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 4.0 | 8.0 | 5.0 | 5.0 | 9.0 | 1.0 | 7.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 2.0 | 9.0 | 1.0 | 2.0 | 0.0 | 2.0 | 0.0 | 0.0 | 0.0 | 2.0 | 2.0 | 4.0 | 2.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 1.0 | 1.0 | 2.0 | 4.0 | 3.0 | 2.0 | 2.0 | 1.0 | 1.0 | 1.0 | 2.0 | 2.0 | 1.0 | 1.0 | 4.0 | 1.0 | 3.0 | 1.0 | 4.0 | 5.0 | 5.0 | 5.0 | 7.0 | 7.0 | 5.0 | 10.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 7.0 | 1.0 | 1.0 | 2.0 | 2.0 | 6.0 | 9.0 | 6.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 9.0 | 5.0 | 5.0 | 5.0 | 5.0 | 7.0 | 4.0 | 3.0 | 3.0 | 4.0 | 3.0 | 5.0 | 1.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 3.0 | 3.0 | 3.0 | 3.0 | 2.0 | 2.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 4.0 | 4.0 | 4.0 | 4.0 | 3.0 | 1.0 | 1.0 | 4.0 | 4.0 | 1.0 | 1.0 | 4.0 | 3.0 | 1.0 | 5.0 | 2.0 | 2.0 | 4.0 | 3.0 | 2.0 | 1.0 | 10.0 | 8.0 | 4.0 | 10.0 | 4.0 | 4.0 | 4.0 | 4.0 | 10.0 | 4.0 | 5.0 | 5.0 | 4.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1957.0 | 64.0 | 5.0 | 3.0 | 1.0 | 1.0 | 1.0 | 504.0 | 504.0 | 504.0 | 1.0 | 6.0 | 1.0 | 170.0 | 1.0 | 4.0 | 0.0 | 1.0 | 0.0 | 1.0 | 0.0 | 1.0 | 0.0 | 1.0 | 5.0 | 4.0 | 0.0 | 3.0 | 4.0 | 2.0 | 2.0 | 2.0 | 3.0 | 1.0 | 1.0 | 5.0 | 50000000.0 | 504005.0 | 4.0 | 2.0 | 2.0 | 2.0 | 4.0 | 4.0 | 2.0 | 2.0 | 2.0 | 2.0 | 4.0 | 2.0 | 4.0 | 2.0 | 2.0 | 4.0 | 4.0 | 4.0 | 4.0 | 2.0 | 2.0 | 2.0 | 4.0 | 4.0 | 2.0 | 4.0 | 4.0 | 4.0 | 2.0 | 4.0 | 4.0 | 2.0 | 2.0 | 4.0 | NaN | NaN | 0.0 | 1.0 | -2.0 | 0.388333 | 0.166667 | 0.270278 | 0.540556 | 0.0 | 0.00 | 0.00 | 0.000000 | 0.00 | 0.0 | 1.000000 | 0.333333 | 1.0 | 1.0 | 1.0 | 1.000000 | 0.00 | 0.33 | 0.33 | 0.220000 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.25 | 0.66 | 1.00 | 0.636667 | 0.444444 | 0.444444 | 0.444444 | 0.444444 | 0.00 | 0.0 | 0.000 | 0.000 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1198 | 0.0 | 0.0 | 2021.0 | 504.0 | MAR | 600.0 | MOR | 504071199.0 | 20211213.0 | 202111.0 | 202112.0 | 17.10 | 18.02 | 52.0 | 2.0 | 504002.0 | 504012.0 | 504081.0 | 5.0 | 3.0 | 3.0 | 1.0 | 81.0 | -2.73 | 34.94 | 170.0 | ar | 1.0 | 2.0 | 1.0 | 0.833333 | 31083.33333 | 1.0 | 3.0 | 2.0 | 4.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1.0 | 1.0 | 2.0 | 1.0 | 2.0 | 2.0 | 2.0 | 2.0 | 1.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 1.0 | 2.0 | 1.0 | 1.0 | 3.0 | 3.0 | 2.0 | 3.0 | 2.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 4.0 | 2.0 | 2.0 | 4.0 | 4.0 | 2.0 | 1.0 | 1.0 | 1.0 | 2.0 | 2.0 | 9.0 | 6.0 | 3.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 1.0 | 2.0 | 2.0 | 4.0 | 3.0 | 4.0 | 4.0 | 4.0 | 2.0 | 2.0 | 2.0 | 2.0 | 3.0 | 2.0 | 2.0 | 4.0 | 4.0 | 4.0 | 2.0 | 2.0 | 4.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 3.0 | 3.0 | 3.0 | 2.0 | 3.0 | 3.0 | 3.0 | 2.0 | 3.0 | 3.0 | 3.0 | 5.0 | 3.0 | 3.0 | 2.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 7.0 | 7.0 | 4.0 | 4.0 | 4.0 | 1.0 | 8.0 | 3.0 | 2.0 | 3.0 | 2.0 | 2.0 | 3.0 | 1.0 | 7.0 | 4.0 | 2.0 | 2.0 | 0.0 | 2.0 | 0.0 | 2.0 | 2.0 | 0.0 | 3.0 | 3.0 | 3.0 | 4.0 | 4.0 | 3.0 | 4.0 | 2.0 | 2.0 | 1.0 | 1.0 | 2.0 | 3.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 1.0 | 1.0 | 2.0 | 1.0 | 4.0 | 1.0 | 2.0 | 6.0 | 6.0 | 4.0 | 4.0 | 5.0 | 7.0 | 9.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 7.0 | 1.0 | 1.0 | 2.0 | 2.0 | 7.0 | 7.0 | 7.0 | 4.0 | 6.0 | 7.0 | 7.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 6.0 | 4.0 | 4.0 | 4.0 | 4.0 | 6.0 | 1.0 | 2.0 | 2.0 | 4.0 | 3.0 | 5.0 | 2.0 | 4.0 | 2.0 | 5.0 | 2.0 | 2.0 | 3.0 | 3.0 | 2.0 | 2.0 | 2.0 | 1.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 4.0 | 4.0 | 4.0 | 2.0 | 3.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 3.0 | 2.0 | 2.0 | 1.0 | 5.0 | 1.0 | 2.0 | 4.0 | 3.0 | 2.0 | 1.0 | 9.0 | 6.0 | 4.0 | 9.0 | 4.0 | 9.0 | 9.0 | 6.0 | 9.0 | 6.0 | 8.0 | 3.0 | 4.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1978.0 | 43.0 | 3.0 | 2.0 | 1.0 | 1.0 | 1.0 | 504.0 | 504.0 | 504.0 | 1.0 | 4.0 | 1.0 | 170.0 | 1.0 | 2.0 | 2.0 | 1.0 | 4.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 5.0 | 2.0 | 0.0 | 3.0 | 6.0 | 2.0 | 2.0 | 2.0 | 3.0 | 3.0 | 1.0 | 5.0 | 50000000.0 | 504005.0 | 4.0 | 2.0 | 2.0 | 4.0 | 4.0 | 4.0 | 4.0 | 2.0 | 2.0 | 4.0 | 4.0 | 4.0 | 4.0 | 2.0 | 2.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 2.0 | 2.0 | 4.0 | 4.0 | 2.0 | 4.0 | 4.0 | 4.0 | 2.0 | 4.0 | 4.0 | 2.0 | 2.0 | 0.0 | NaN | NaN | 2.0 | 2.0 | -1.0 | 0.443333 | 0.221667 | 0.283194 | 0.483889 | 0.0 | 0.33 | 0.00 | 0.110000 | 0.00 | 0.0 | 1.000000 | 0.333333 | 1.0 | 1.0 | 1.0 | 1.000000 | 0.33 | 0.33 | 0.33 | 0.330000 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.25 | 0.66 | 0.66 | 0.523333 | 0.666667 | 0.333333 | 0.333333 | 0.444444 | 0.33 | 0.0 | 0.165 | 0.165 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1.0 |
| 1199 | 0.0 | 0.0 | 2021.0 | 504.0 | MAR | 600.0 | MOR | 504071200.0 | 20211213.0 | 202111.0 | 202112.0 | 17.40 | 18.13 | 34.0 | 2.0 | 504002.0 | 504012.0 | 504081.0 | 5.0 | 3.0 | 3.0 | 1.0 | 81.0 | -2.73 | 34.94 | 170.0 | ar | 1.0 | 1.0 | 1.0 | 0.833333 | 31083.33333 | 1.0 | 1.0 | 2.0 | 4.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1.0 | 1.0 | 2.0 | 2.0 | 1.0 | 1.0 | 2.0 | 2.0 | 2.0 | 1.0 | 2.0 | 2.0 | 2.0 | 1.0 | 2.0 | 1.0 | 2.0 | 2.0 | 1.0 | 3.0 | 1.0 | 3.0 | 3.0 | 1.0 | 4.0 | 2.0 | 1.0 | 1.0 | 3.0 | 3.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 3.0 | 1.0 | 2.0 | 2.0 | 1.0 | 7.0 | 8.0 | 7.0 | 4.0 | 2.0 | 4.0 | 4.0 | 4.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 2.0 | 2.0 | 2.0 | 3.0 | 1.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 2.0 | 2.0 | 2.0 | 3.0 | 2.0 | 3.0 | 3.0 | 3.0 | 3.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 3.0 | 3.0 | 5.0 | 2.0 | 3.0 | 3.0 | 1.0 | 2.0 | 2.0 | 0.0 | 1.0 | 0.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 0.0 | 10.0 | 10.0 | 8.0 | 1.0 | 1.0 | 2.0 | 8.0 | 3.0 | 2.0 | 3.0 | 3.0 | 3.0 | 4.0 | 3.0 | 4.0 | 3.0 | 2.0 | 2.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 2.0 | 2.0 | 3.0 | 2.0 | 1.0 | 2.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 2.0 | 3.0 | 1.0 | 2.0 | 1.0 | 2.0 | 3.0 | 4.0 | 1.0 | 2.0 | 1.0 | 1.0 | 2.0 | 1.0 | 3.0 | 1.0 | 2.0 | 10.0 | 10.0 | 7.0 | 7.0 | 3.0 | 5.0 | 10.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 6.0 | 6.0 | 2.0 | 1.0 | 2.0 | 9.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 7.0 | 7.0 | 1.0 | 1.0 | 1.0 | 1.0 | 8.0 | 1.0 | 1.0 | 1.0 | 1.0 | 8.0 | 2.0 | 4.0 | 2.0 | 2.0 | 3.0 | 1.0 | 5.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 3.0 | 2.0 | 2.0 | 2.0 | 1.0 | 3.0 | 3.0 | 3.0 | 1.0 | 2.0 | 3.0 | 3.0 | 3.0 | 3.0 | 4.0 | 4.0 | 4.0 | 3.0 | 4.0 | 1.0 | 1.0 | 3.0 | 3.0 | 4.0 | 3.0 | 3.0 | 3.0 | 1.0 | 4.0 | 3.0 | 2.0 | 4.0 | 2.0 | 1.0 | 7.0 | 8.0 | 9.0 | 10.0 | 8.0 | 1.0 | 9.0 | 7.0 | 8.0 | 9.0 | 9.0 | 7.0 | 5.0 | 2.0 | 2.0 | 1.0 | 2.0 | 2.0 | 3.0 | 3.0 | 1.0 | 1998.0 | 23.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 504.0 | 504.0 | 504.0 | 1.0 | 3.0 | 1.0 | 170.0 | 6.0 | 0.0 | 4.0 | 2.0 | NaN | NaN | 2.0 | 1.0 | 2.0 | 1.0 | 7.0 | NaN | 0.0 | NaN | 5.0 | 3.0 | 2.0 | 2.0 | 3.0 | 7.0 | 2.0 | 5.0 | 50000000.0 | 504005.0 | 2.0 | 2.0 | 2.0 | 4.0 | 4.0 | 2.0 | 5.0 | 2.0 | 2.0 | 4.0 | 5.0 | 2.0 | 2.0 | 5.0 | 2.0 | 2.0 | 4.0 | 2.0 | 5.0 | 5.0 | 3.0 | 2.0 | 4.0 | 4.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 4.0 | 4.0 | 2.0 | 2.0 | 4.0 | 2.0 | 2.0 | 1.0 | 1.0 | 1.0 | 0.331944 | 0.443889 | 0.311944 | 0.457222 | 0.5 | 0.33 | 0.00 | 0.276667 | 0.00 | 1.0 | 0.833333 | 0.611111 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.00 | 0.66 | 0.66 | 0.440000 | 0.0 | 0.0 | 1.0 | 0.333333 | 0.75 | 0.00 | 0.66 | 0.470000 | 0.000000 | 0.666667 | 0.666667 | 0.444444 | 0.00 | 0.0 | 0.000 | 0.000 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
1200 rows × 430 columns
# setuping the variable view
variable_view = pd.DataFrame({
"Variable Name": meta.column_names,
"Label": meta.column_labels,
"Values": [meta.variable_value_labels.get(var, {}) for var in meta.column_names]
})
variable_view.head(10)
| Variable Name | Label | Values | |
|---|---|---|---|
| 0 | version | Version of Data File | {} |
| 1 | doi | Digital Object Identifier | {} |
| 2 | A_YEAR | Year of survey | {2017.0: '2017', 2018.0: '2018', 2019.0: '2019... |
| 3 | B_COUNTRY | ISO 3166-1 numeric country code | {504.0: 'Morocco'} |
| 4 | B_COUNTRY_ALPHA | ISO 3166-1 alpha-3 country code | {} |
| 5 | C_COW_NUM | CoW country code numeric | {600.0: 'Morocco'} |
| 6 | C_COW_ALPHA | CoW country code alpha | {} |
| 7 | D_INTERVIEW | Interview ID | {} |
| 8 | J_INTDATE | Date of interview | {-5.0: 'No answer; Missing; GB,NIRL: Postal su... |
| 9 | FW_START | Year/month of start-fieldwork | {} |
# seting up the labeled view dataframe: labeled_df
labeled_df = df.copy()
for column, value_labels in meta.variable_value_labels.items():
if column in labeled_df.columns:
# Ensure consistent types between DataFrame values and value_labels keys
value_labels = {float(k): v for k, v in value_labels.items()} # Convert keys to float
labeled_df[column] = labeled_df[column].map(value_labels).fillna(labeled_df[column])
labeled_df.head(5)
| version | doi | A_YEAR | B_COUNTRY | B_COUNTRY_ALPHA | C_COW_NUM | C_COW_ALPHA | D_INTERVIEW | J_INTDATE | FW_START | FW_END | K_TIME_START | K_TIME_END | K_DURATION | Q_MODE | N_REGION_ISO | N_REGION_WVS | N_TOWN | G_TOWNSIZE | G_TOWNSIZE2 | H_SETTLEMENT | H_URBRURAL | I_PSU | O1_LONGITUDE | O2_LATITUDE | S_INTLANGUAGE | LNGE_ISO | E_RESPINT | F_INTPRIVACY | W_WEIGHT | S018 | PWGHT | Q1 | Q2 | Q3 | Q4 | Q5 | Q6 | Q7 | Q8 | Q9 | Q10 | Q11 | Q12 | Q13 | Q14 | Q15 | Q16 | Q17 | Q18 | Q19 | Q20 | Q21 | Q22 | Q23 | Q24 | Q25 | Q26 | Q27 | Q28 | Q29 | Q30 | Q31 | Q32 | Q33 | Q33_3 | Q34 | Q34_3 | Q35 | Q35_3 | Q37 | Q38 | Q39 | Q40 | Q41 | Q42 | Q43 | Q44 | Q45 | Q46 | Q47 | Q48 | Q49 | Q50 | Q51 | Q52 | Q53 | Q54 | Q55 | Q56 | Q57 | Q58 | Q59 | Q60 | Q61 | Q62 | Q63 | Q64 | Q65 | Q66 | Q67 | Q68 | Q69 | Q70 | Q71 | Q72 | Q73 | Q74 | Q75 | Q76 | Q77 | Q78 | Q79 | Q80 | Q81 | Q82 | Q82_ARABLEAGUE | Q82_GULFCOOP | Q82_ISLCOOP | Q83 | Q84 | Q85 | Q86 | Q87 | Q88 | Q89 | Q90 | Q91 | Q92 | Q93 | Q94 | Q95 | Q96 | Q97 | Q98 | Q99 | Q100 | Q101 | Q102 | Q103 | Q104 | Q105 | Q106 | Q107 | Q108 | Q109 | Q110 | Q111 | Q112 | Q113 | Q114 | Q115 | Q116 | Q117 | Q118 | Q119 | Q120 | Q121 | Q122 | Q123 | Q124 | Q125 | Q126 | Q127 | Q128 | Q129 | Q130 | Q131 | Q132 | Q133 | Q134 | Q135 | Q136 | Q137 | Q138 | Q139 | Q140 | Q141 | Q142 | Q143 | Q144 | Q145 | Q146 | Q147 | Q148 | Q149 | Q150 | Q151 | Q152 | Q153 | Q154 | Q155 | Q156 | Q157 | Q158 | Q159 | Q160 | Q161 | Q162 | Q163 | Q164 | Q165 | Q166 | Q167 | Q168 | Q169 | Q170 | Q171 | Q172 | Q173 | Q174 | Q175 | Q176 | Q177 | Q178 | Q179 | Q180 | Q181 | Q182 | Q183 | Q184 | Q185 | Q186 | Q187 | Q188 | Q189 | Q190 | Q191 | Q192 | Q193 | Q194 | Q195 | Q196 | Q197 | Q198 | Q199 | Q200 | Q201 | Q202 | Q203 | Q204 | Q205 | Q206 | Q207 | Q208 | Q209 | Q210 | Q211 | Q212 | Q213 | Q214 | Q215 | Q216 | Q217 | Q218 | Q219 | Q220 | Q221 | Q222 | Q223 | Q223_ABREV | Q223_LOCAL | Q224 | Q225 | Q226 | Q227 | Q228 | Q229 | Q230 | Q231 | Q232 | Q233 | Q234 | Q234A | Q235 | Q236 | Q237 | Q238 | Q239 | Q240 | Q241 | Q242 | Q243 | Q244 | Q245 | Q246 | Q247 | Q248 | Q249 | Q250 | Q251 | Q252 | Q253 | Q254 | Q255 | Q256 | Q257 | Q258 | Q259 | Q260 | Q261 | Q262 | X003R | X003R2 | Q263 | Q264 | Q265 | Q266 | Q267 | Q268 | Q269 | Q270 | Q271 | Q272 | Q273 | Q274 | Q275 | Q275R | Q276 | Q276R | Q277 | Q277R | Q278 | Q278R | Q279 | Q280 | Q281 | Q282 | Q283 | Q284 | Q285 | Q286 | Q287 | Q288 | Q288R | Q289 | Q289CS9 | Q290 | Q291G1 | Q291G2 | Q291G3 | Q291G4 | Q291G5 | Q291G6 | Q291P1 | Q291P2 | Q291P3 | Q291P4 | Q291P5 | Q291P6 | Q291UN1 | Q291UN2 | Q291UN3 | Q291UN4 | Q291UN5 | Q291UN6 | Q292A | Q292B | Q292C | Q292D | Q292E | Q292F | Q292G | Q292H | Q292I | Q292J | Q292K | Q292L | Q292M | Q292N | Q292O | Q293 | Q294A | Q294B | Y001 | Y002 | Y003 | SACSECVAL | SACSECVALB | RESEMAVAL | RESEMAVALB | I_AUTHORITY | I_NATIONALISM | I_DEVOUT | DEFIANCE | I_RELIGIMP | I_RELIGBEL | I_RELIGPRAC | DISBELIEF | I_NORM1 | I_NORM2 | I_NORM3 | RELATIVISM | I_TRUSTARMY | I_TRUSTPOLICE | I_TRUSTCOURTS | SCEPTICISM | I_INDEP | I_IMAGIN | I_NONOBED | AUTONOMY | I_WOMJOB | I_WOMPOL | I_WOMEDU | EQUALITY | I_HOMOLIB | I_ABORTLIB | I_DIVORLIB | CHOICE | I_VOICE1 | I_VOICE2 | I_VOI2_00 | VOICE | SECVALWGT | WEIGHT1A | WEIGHT1B | WEIGHT2A | WEIGHT2B | WEIGHT3A | WEIGHT3B | WEIGHT4A | WEIGHT4B | RESEMAVALBWGT | RESEMAVALWGT | SECVALBWGT | Y001_1 | Y001_2 | Y001_3 | Y001_4 | Y001_5 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.0 | 0.0 | 2021 | Morocco | MAR | Morocco | MOR | 504070001.0 | 20211112.0 | 202111.0 | 202112.0 | 10.05 | 10.43 | 38.0 | Paper-and-Pencil Interviewing (PAPI) | MA-09 Souss-Massa | MA: MA-09 Souss-Massa | MA: Afella Ighir | Under 2,000 | Under 5,000 | Another city, town (not a regional or district... | Rural | 99.0 | -8.84 | 29.49 | Arabic | ar | Respondent was very interested | There were other people around who could follo... | No weighting | 0.833333 | 31083.33333 | Very important | Rather important | Very important | Very important | Very important | Very important | Important | Not mentioned | Important | Important | Not mentioned | Not mentioned | Not mentioned | Not mentioned | Important | Not mentioned | Important | Mentioned | Not mentioned | Not mentioned | Mentioned | Mentioned | Mentioned | Not mentioned | Mentioned | Not mentioned | Agree strongly | Agree | Agree strongly | Disagree | Agree strongly | Agree strongly | Agree | Agree | Agree strongly | Agree | Strongly agree | Agree | Agree | Agree strongly | Strongly agree | Strongly agree | Neither agree nor disagree | Our society must be gradually improved by reforms | Don't mind | Good thing | Bad thing | Quite happy | Fair | 5 | 8 | 6 | Never | Rarely | Never | Never | Never | Or about the same | Need to be very careful | Trust completely | Do not trust very much | Trust somewhat | Do not trust at all | Do not trust at all | Trust somewhat | Quite a lot | Not very much | Not very much | Quite a lot | Not very much | Not very much | A great deal | None at all | None at all | None at all | Not very much | Quite a lot | A great deal | A great deal | Not very much | A great deal | Quite a lot | Not very much | Quite a lot | Quite a lot | Quite a lot | Quite a lot | Quite a lot | Not very much | Quite a lot | Not very much | None at all | None at all | Quite a lot | 5 | India | Geneva | Human rights | Inactive member | Don't belong | Don't belong | Not a member | Not a member | Don't belong | Not a member | Not a member | Don't belong | Don't belong | Don't belong | Don't belong | 6 | 4 | 5 | 5 | 5 | Economy growth and creating jobs | 10 There is abundant corruption in my country | Most of them | Most of them | Few of them | Few of them | Most of them | Frequently | Disagree | 8 | Neither good, nor bad | Agree | Agree | Hard to say | Agree | Hard to say | Agree | Hard to say | Disagree | Place strict limits on the number of foreigner... | Quite secure | Not frequently | Not frequently | Quite frequently | Quite frequently | Not frequently | Not frequently | Not frequently | Yes | No | Yes | A great deal | A great deal | No | No | Very much | Very much | A great deal | Equality | Security | No | A high level of economic growth | Seeing that people have more say about how ar... | Maintaining order in the nation | Protecting freedom of speech | Progress toward a less impersonal and more hum... | The fight against crime | 6 | 8 | 8 | 5 | 6 | 8 | Very important | Yes | Yes | Yes | Yes | Strongly agree | Strongly agree | More than once a week | Several times a day | Not a religious person | Do good to other people | Make sense of life after death | 4 | 5 | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | 7 | 2 | Never justifiable | 6 | 2 | 8 | Never justifiable | Never justifiable | Never justifiable | 3 | Always justifiable | Definitely should have the right | Definitely should not have the right | Definitely should not have the right | Somewhat interested | Never | Never | Daily | Weekly | Weekly | Less than monthly | Weekly | Weekly | Never | Have done | Have done | Would never do | Would never do | Might do | Would never do | Might do | Would never do | Might do | Would never do | Would never do | Would never do | Usually | Usually | MAR: Authenticity and Modernity Party | MAR: PAM | MAR: Parti de l'Authenticite et de la Modernit... | Very often | Not often | Fairly often | Very often | Very often | Very often | Very often | Not at all often | Not at all often | Not often | Rather important | Not at all | Very bad | Very bad | Very bad | Fairly good | Very good | 8 | An essential characteristic of democracy | 7 | 6 | 9 | 4 | 8 | 9 | 5 | An essential characteristic of democracy | Absolutely important | 3 | Not satisfied at all | Not much respect | Not very proud | Not close at all | Not close at all | Not close at all | Not very close | Not close at all | Female | 1969.0 | 52.0 | 45-54 | 50 and more years | I am born in this country | Not an immigrant | Not an immigrant | Morocco | Morocco | Morocco | Yes | 8.0 | No | Berber; Amazigh;Tamaziɣt | Married | 4 children | Primary education (ISCED 1) | Lower | Early childhood education (ISCED 0) / no educa... | Lower | Early childhood education (ISCED 0) / no educa... | Lower | Early childhood education (ISCED 0) / no educa... | Lower | Full time (30 hours a week or more) | Homemaker not otherwise employed | Service (for example: restaurant owner, police... | Farm worker (for example: farm labourer, tract... | Farm worker (for example: farm labourer, tract... | Private business or industry | Yes | Spent some savings | Upper middle class | Sixth step | Medium | Muslim | Islam; nfd | MA: Arabe | Disagree strongly | Agree | Disagree strongly | Disagree | Disagree strongly | Disagree strongly | Disagree strongly | Agree | Disagree strongly | Disagree | Disagree strongly | Disagree | Neither agree nor disagree | Disagree | Disagree | Agree | Disagree strongly | Neither agree nor disagree | Agree strongly | Agree | Neither agree nor disagree | Disagree | Disagree | Agree | Disagree | Disagree | Disagree strongly | Disagree strongly | Disagree strongly | Agree strongly | Agree | Disagree strongly | Neither agree nor disagree | 1 | 1 | 1 | 3 | Mixed | Obedience/Religious Faith | 0.331667 | 0.443333 | 0.235139 | 0.262778 | High | High | Very low | 0.553333 | Very low | Not religious or atheist | 0.000000 | 0.333333 | Conformist | Conformist | Conformist | 0.000000 | Low | Low | Very high | 0.44 | No | No | No | 0.000000 | Medium-Low | Very low | High | 0.303333 | 0.000000 | 0.000000 | 0.666667 | 0.222222 | High | Medio | 0.415 | 0.415 | 1.0 | Complete | Complete | Complete | Complete | Complete | Complete | Complete | Complete | 1.0 | 1.0 | 1.0 | 1 | 0 | 1 | 1 | 0 |
| 1 | 0.0 | 0.0 | 2021 | Morocco | MAR | Morocco | MOR | 504070002.0 | 20211112.0 | 202111.0 | 202112.0 | 10.46 | 11.19 | 33.0 | Paper-and-Pencil Interviewing (PAPI) | MA-09 Souss-Massa | MA: MA-09 Souss-Massa | MA: Afella Ighir | Under 2,000 | Under 5,000 | Another city, town (not a regional or district... | Rural | 99.0 | -8.84 | 29.49 | Arabic | ar | Respondent was very interested | There were no other people around who could fo... | No weighting | 0.833333 | 31083.33333 | Very important | Rather important | Not very important | Not at all important | Very important | Very important | Important | Not mentioned | Important | Not mentioned | Important | Not mentioned | Important | Not mentioned | Important | Not mentioned | Not mentioned | Not mentioned | Not mentioned | Mentioned | Mentioned | Mentioned | Not mentioned | Not mentioned | Not mentioned | Not mentioned | Agree strongly | Agree strongly | Agree strongly | Disagree | Agree strongly | Agree strongly | Agree strongly | Agree | Agree strongly | Agree | Disagree strongly | Disagree | Agree strongly | Agree strongly | Strongly agree | Strongly agree | Agree strongly | Our present society must be valiantly defended... | Bad thing | Good thing | Good thing | Not at all happy | Poor | 5 | 4 | 2 | Never | Often | Often | Sometimes | Never | Or about the same | Need to be very careful | Trust completely | Do not trust very much | Do not trust at all | Do not trust at all | Do not trust at all | Do not trust very much | A great deal | A great deal | None at all | None at all | Not very much | A great deal | Not very much | None at all | None at all | None at all | Not very much | Not very much | None at all | None at all | None at all | Quite a lot | Quite a lot | A great deal | Not very much | Not very much | Not very much | Quite a lot | None at all | None at all | None at all | None at all | None at all | None at all | None at all | 5 | India | London | Human rights | Active member | Don't belong | Active member | Not a member | Not a member | Don't belong | Not a member | Not a member | Don't belong | Don't belong | Don't belong | Don't belong | There should be greater incentives for individ... | Government ownership of business should be inc... | 9 | 6 | 6 | Protecting environment | 10 There is abundant corruption in my country | Most of them | All of them | Most of them | None of them | All of them | Always | Hard to say | Very high risk | Rather bad | Agree | Agree | Disagree | Disagree | Agree | Agree | Agree | Agree | Prohibit people coming here from other countries | Quite secure | Very Frequently | Very Frequently | Very Frequently | Very Frequently | Quite frequently | Very Frequently | Very Frequently | Yes | Yes | No | Very much | Very much | Yes | Yes | Very much | Very much | Very much | Freedom | Security | Yes | A high level of economic growth | Trying to make our cities and countryside more... | Maintaining order in the nation | Fighting rising prices | Progress toward a less impersonal and more hum... | Progress toward a society in which Ideas count... | Completely agree | Completely agree | Completely agree | Completely agree | 8 | A lot worse off | Not at all important | Yes | No | No | No | Strongly disagree | Strongly disagree | Once a year | Only on special holy days | A religious person | Do good to other people | Make sense of life in this world | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 8 | 5 | 5 | 5 | 5 | 9 | 5 | 5 | 5 | 5 | 6 | Definitely should have the right | Definitely should not have the right | Definitely should not have the right | Somewhat interested | Occasionally | Less than monthly | Less than monthly | Never | Daily | Never | Daily | Daily | Less than monthly | Might do | Have done | Might do | Might do | Have done | Might do | Might do | Would never do | Might do | Might do | Would never do | Would never do | Never | Never | MAR: Justice and Development Party | MAR: PJD | MAR: Parti de la Justice et du Developpement -... | Not at all often | Not often | Not at all often | Not at all often | Not at all often | Not at all often | Very often | Very often | Not often | Fairly often | Very important | Not at all | Very good | Very good | Very bad | Very good | Very good | Left | An essential characteristic of democracy | An essential characteristic of democracy | An essential characteristic of democracy | An essential characteristic of democracy | An essential characteristic of democracy | An essential characteristic of democracy | An essential characteristic of democracy | An essential characteristic of democracy | 5 | 8 | 6 | 7 | Not much respect | Very proud | Very close | Very close | Close | Not very close | Not close at all | Female | 1998.0 | 23.0 | 16-24 | 16-29 years | I am born in this country | Not an immigrant | Not an immigrant | Morocco | Morocco | Morocco | Yes | 6 | Yes, own parent(s) | Berber; Amazigh;Tamaziɣt | Single | No children | Bachelor or equivalent (ISCED 6) | Higher | Upper secondary education (ISCED 3) | Middle | Early childhood education (ISCED 0) / no educa... | Lower | Early childhood education (ISCED 0) / no educa... | Lower | Full time (30 hours a week or more) | NaN | Sales (for example: sales manager, shop owner,... | NaN | Skilled worker (for example: foreman, motor me... | Private non-profit organization | No | Just get by | Working class | Seventh step | Medium | Muslim | Islam; nfd | MA: Arabe | Disagree strongly | Agree strongly | Disagree strongly | Disagree strongly | Disagree strongly | Disagree strongly | Agree strongly | Agree strongly | Agree strongly | Disagree strongly | Disagree strongly | Disagree strongly | Agree strongly | Disagree strongly | Neither agree nor disagree | Neither agree nor disagree | Neither agree nor disagree | Agree | Agree strongly | Agree strongly | Disagree strongly | Disagree strongly | Agree strongly | Agree | Disagree | Disagree strongly | Disagree strongly | Disagree strongly | Disagree strongly | Disagree strongly | Agree strongly | Disagree strongly | Disagree strongly | No trust at all | NaN | NaN | 2 | Materialist | -1 | 0.360556 | 0.111111 | 0.360556 | 0.387778 | Low | Very low | Very low | 0.000000 | Very low | Religious | 0.666667 | 0.222222 | Inconformist | Inconformist | Inconformist | 1.000000 | Very high | Very high | Low | 0.22 | No | Yes | Yes | 0.666667 | Low | Very low | High | 0.220000 | 0.444444 | 0.444444 | 0.777778 | 0.555556 | Very high | Bajo | 0.000 | 0.000 | 1.0 | Complete | Complete | Complete | Complete | Complete | Complete | Complete | Complete | 1.0 | 1.0 | 1.0 | 0 | 0 | 0 | 1 | 1 |
| 2 | 0.0 | 0.0 | 2021 | Morocco | MAR | Morocco | MOR | 504070003.0 | 20211112.0 | 202111.0 | 202112.0 | 11.28 | 12.09 | 41.0 | Paper-and-Pencil Interviewing (PAPI) | MA-09 Souss-Massa | MA: MA-09 Souss-Massa | MA: Afella Ighir | Under 2,000 | Under 5,000 | Another city, town (not a regional or district... | Rural | 99.0 | -8.84 | 29.49 | Arabic | ar | Respondent was very interested | There were other people around who could follo... | No weighting | 0.833333 | 31083.33333 | Very important | Rather important | Very important | Not at all important | Very important | Very important | Important | Not mentioned | Important | Important | Not mentioned | Important | Not mentioned | Important | Important | Not mentioned | Not mentioned | Mentioned | Not mentioned | Not mentioned | Not mentioned | Mentioned | Not mentioned | Mentioned | Not mentioned | Not mentioned | Agree strongly | Agree strongly | Agree strongly | Disagree | Agree | Disagree | Neither agree nor disagree | Neither agree nor disagree | Agree strongly | Agree | Strongly agree | Agree | Agree strongly | Agree strongly | Strongly agree | Strongly agree | Agree strongly | The entire way our society is organized must b... | Bad thing | Good thing | Good thing | Not very happy | Poor | 2 | 2 | 3 | Rarely | Rarely | Sometimes | Often | Often | Worse off | Need to be very careful | Trust completely | Do not trust at all | Do not trust at all | Do not trust at all | Do not trust at all | Do not trust at all | Quite a lot | A great deal | Quite a lot | Not very much | None at all | A great deal | A great deal | Not very much | Not very much | None at all | A great deal | Quite a lot | None at all | A great deal | A great deal | A great deal | Not very much | Not very much | None at all | None at all | None at all | None at all | Not very much | None at all | None at all | None at all | A great deal | Quite a lot | A great deal | 9 | India | Geneva | Human rights | Don't belong | Active member | Don't belong | Not a member | Not a member | Don't belong | Not a member | Not a member | Don't belong | Don't belong | Don't belong | Don't belong | Incomes should be made more equal | Private ownership of business should be increased | The government should take more responsibility... | Competition is good | In the long run, hard work usually brings a be... | Protecting environment | 10 There is abundant corruption in my country | Few of them | Most of them | Few of them | Few of them | Most of them | Always | Strongly disagree | Very high risk | Quite good | Disagree | Agree | Agree | Agree | Disagree | Agree | Agree | Disagree | Let people come as long as there are jobs avai... | Quite secure | Not frequently | Not at all frequently | Not at all frequently | Quite frequently | Not frequently | Not frequently | Very Frequently | Yes | Yes | No | Not at all | A great deal | Yes | Yes | Very much | Very much | Very much | Equality | Freedom | Yes | A high level of economic growth | Making sure this country has strong defence fo... | Maintaining order in the nation | Protecting freedom of speech | Progress toward a less impersonal and more hum... | Progress toward a society in which Ideas count... | 8 | 8 | 7 | 5 | 2 | 7 | Very important | Yes | Yes | Yes | Yes | Strongly agree | Strongly agree | Once a week | Several times a day | Not a religious person | Follow religious norms and ceremonies | Make sense of life after death | 4 | 7 | 2 | 2 | 2 | Never justifiable | Never justifiable | Never justifiable | 4 | 7 | 2 | Never justifiable | Never justifiable | 5 | 3 | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Definitely should have the right | Probably should not have the right | Probably should not have the right | Not at all interested | Never | Never | Never | Never | Never | Never | Never | Weekly | Weekly | Would never do | Would never do | Would never do | Would never do | Would never do | Would never do | Would never do | Would never do | Might do | Would never do | Would never do | Would never do | Never | Never | None | 4.0 | 4.0 | Very often | Not at all often | Fairly often | Not often | Not often | Very often | Fairly often | Not often | Not often | Very often | Not very important | Very little | Very good | Very good | Fairly bad | Fairly good | Fairly good | 8 | An essential characteristic of democracy | 7 | An essential characteristic of democracy | An essential characteristic of democracy | 5 | An essential characteristic of democracy | 6 | 8 | An essential characteristic of democracy | Absolutely important | 6 | 6 | Fairly much respect | Quite proud | Very close | Close | Not close at all | Not close at all | Not close at all | Male | 1986.0 | 35.0 | 35-44 | 30-49 years | I am born in this country | Not an immigrant | Not an immigrant | Morocco | Morocco | Morocco | Yes | 4 | Yes, own parent(s) | Arabic | Divorced | 1 child | Bachelor or equivalent (ISCED 6) | Higher | Post-secondary non-tertiary education (ISCED 4) | Middle | Bachelor or equivalent (ISCED 6) | Higher | Bachelor or equivalent (ISCED 6) | Higher | Unemployed | NaN | Farm worker (for example: farm labourer, tract... | Farm worker (for example: farm labourer, tract... | Farm worker (for example: farm labourer, tract... | Private non-profit organization | Yes | Just get by | Upper middle class | Seventh step | Medium | Muslim | Islam; nfd | MA: Arabe | Disagree strongly | Disagree | Agree strongly | Disagree | Disagree | Agree strongly | Disagree strongly | Disagree strongly | Agree | Disagree | Disagree | Agree | Agree | Neither agree nor disagree | Agree | Neither agree nor disagree | Agree | Agree strongly | Agree strongly | Agree strongly | Neither agree nor disagree | Disagree | Agree | Agree strongly | Disagree strongly | Neither agree nor disagree | Agree strongly | Agree | Disagree strongly | Agree | Agree | Agree | Disagree | 7 | NaN | NaN | 3 | Mixed | 0 | 0.291389 | 0.249444 | 0.304583 | 0.360000 | Low | Low | Very low | 0.110000 | Very low | Not religious or atheist | 0.166667 | 0.388889 | Inconformist | Inconformist | Conformist | 0.666667 | Very high | Very high | Very high | 0.00 | No | No | Yes | 0.333333 | Medium | Very low | High | 0.386667 | 0.000000 | 0.333333 | 0.666667 | 0.333333 | High | Bajo | 0.165 | 0.165 | 1.0 | Complete | Complete | Complete | Complete | Complete | Complete | Complete | Complete | 1.0 | 1.0 | 1.0 | 0 | 0 | 1 | 1 | 1 |
| 3 | 0.0 | 0.0 | 2021 | Morocco | MAR | Morocco | MOR | 504070004.0 | 20211112.0 | 202111.0 | 202112.0 | 12.08 | 12.42 | 34.0 | Paper-and-Pencil Interviewing (PAPI) | MA-09 Souss-Massa | MA: MA-09 Souss-Massa | MA: Afella Ighir | Under 2,000 | Under 5,000 | Another city, town (not a regional or district... | Rural | 99.0 | -8.84 | 29.49 | Arabic | ar | Respondent was very interested | There were no other people around who could fo... | No weighting | 0.833333 | 31083.33333 | Very important | Very important | Not at all important | Rather important | Very important | Very important | Important | Not mentioned | Important | Not mentioned | Not mentioned | Important | Not mentioned | Not mentioned | Not mentioned | Important | Important | Not mentioned | Not mentioned | Not mentioned | Not mentioned | Mentioned | Not mentioned | Not mentioned | Not mentioned | Not mentioned | Agree strongly | Agree strongly | Agree strongly | Disagree | Disagree | Agree strongly | Agree strongly | Agree | Agree strongly | Agree | Strongly agree | Agree | Agree strongly | Agree strongly | Strongly agree | Strongly agree | Agree strongly | Our present society must be valiantly defended... | Bad thing | Good thing | Good thing | Quite happy | Very good | 8 | Completely dissatisfied | 6 | Never | Rarely | Never | Never | Never | Or about the same | Need to be very careful | Trust completely | Do not trust very much | Do not trust very much | Do not trust at all | Do not trust very much | Do not trust very much | Not very much | A great deal | None at all | None at all | None at all | A great deal | A great deal | Not very much | None at all | None at all | None at all | None at all | None at all | None at all | None at all | None at all | None at all | None at all | Not very much | Not very much | Not very much | Not very much | Not very much | None at all | None at all | Not very much | None at all | None at all | None at all | Being democratic | India | Geneva | Human rights | Don't belong | Don't belong | Don't belong | Not a member | Not a member | Don't belong | Not a member | Not a member | Don't belong | Don't belong | Don't belong | Don't belong | There should be greater incentives for individ... | Government ownership of business should be inc... | People should take more responsibility to prov... | Competition is harmful | Hard work doesn't generally bring success - it... | Economy growth and creating jobs | 7 | Few of them | Most of them | Few of them | Few of them | Most of them | Frequently | Strongly agree | Very high risk | Quite good | Agree | Disagree | Hard to say | Hard to say | Disagree | Disagree | Agree | Disagree | Place strict limits on the number of foreigner... | Very Secure | Not frequently | Quite frequently | Quite frequently | Not at all frequently | Quite frequently | Not frequently | Very Frequently | Yes | Yes | Yes | Very much | Very much | No | No | Very much | Very much | Very much | Equality | Freedom | Yes | A high level of economic growth | Trying to make our cities and countryside more... | Maintaining order in the nation | Fighting rising prices | A stable economy | Progress toward a society in which Ideas count... | Completely agree | Completely agree | Completely agree | 8 | Completely disagree | A lot better off | Very important | Yes | Yes | Yes | Yes | Strongly agree | Strongly agree | More than once a week | Several times a day | A religious person | Do good to other people | Make sense of life in this world | Completely agree | 5 | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Definitely should have the right | Definitely should not have the right | Definitely should not have the right | Somewhat interested | Occasionally | Less than monthly | Daily | Less than monthly | Daily | Daily | Daily | Daily | Daily | Would never do | Have done | Would never do | Would never do | Have done | Might do | Have done | Have done | Have done | Have done | Have done | Have done | Usually | Usually | None | 4.0 | 4.0 | Not often | Not often | Very often | Very often | Not often | Fairly often | Fairly often | Fairly often | Very often | Fairly often | Very important | Some | Very good | Very bad | Very bad | Very bad | Bad | 5 | An essential characteristic of democracy | An essential characteristic of democracy | An essential characteristic of democracy | An essential characteristic of democracy | Not an essential characteristic of democracy | An essential characteristic of democracy | An essential characteristic of democracy | Not an essential characteristic of democracy | Not an essential characteristic of democracy | Absolutely important | 6 | 6 | A great deal of respect | Very proud | Close | Not close at all | Not close at all | Not close at all | Not close at all | Male | 1993.0 | 28.0 | 25-34 | 16-29 years | I am born in this country | Not an immigrant | Not an immigrant | Morocco | Morocco | Morocco | Yes | 4 | Yes, own parent(s) | Arabic | Married | 2 children | Primary education (ISCED 1) | Lower | Early childhood education (ISCED 0) / no educa... | Lower | Lower secondary education (ISCED 2) | Lower | Lower secondary education (ISCED 2) | Lower | Unemployed | Full time (30 hours a week or more) | Clerical (for example: secretary, clerk, offic... | Never had a job | Farm worker (for example: farm labourer, tract... | Private business or industry | No | Just get by | Lower middle class | Sixth step | Medium | Muslim | Islam; nfd | MA: Arabe | Agree | Agree strongly | Agree strongly | Agree | Disagree strongly | Neither agree nor disagree | Disagree | Agree strongly | Agree strongly | Agree | Disagree strongly | Disagree | Agree strongly | Agree strongly | Agree strongly | Neither agree nor disagree | Disagree | Disagree | Agree | Agree | Agree | Agree | Agree | Disagree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | 5 | NaN | NaN | 1 | Materialist | -1 | 0.000000 | 0.000000 | 0.055000 | 0.110000 | Low | Very low | Very low | 0.000000 | Very low | Religious | 0.000000 | 0.000000 | Conformist | Conformist | Conformist | 0.000000 | Very high | Very high | Very high | 0.00 | No | No | No | 0.000000 | Low | Very low | High | 0.220000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | Very high | Bajo | 0.000 | 0.000 | 1.0 | Complete | Complete | Complete | Complete | Complete | Complete | Complete | Complete | 1.0 | 1.0 | 1.0 | 0 | 0 | 0 | 0 | 1 |
| 4 | 0.0 | 0.0 | 2021 | Morocco | MAR | Morocco | MOR | 504070005.0 | 20211112.0 | 202111.0 | 202112.0 | 13.18 | 13.55 | 37.0 | Paper-and-Pencil Interviewing (PAPI) | MA-09 Souss-Massa | MA: MA-09 Souss-Massa | MA: Afella Ighir | Under 2,000 | Under 5,000 | Another city, town (not a regional or district... | Rural | 99.0 | -8.84 | 29.49 | Arabic | ar | Respondent was very interested | There were no other people around who could fo... | No weighting | 0.833333 | 31083.33333 | Very important | Rather important | Not very important | Not very important | Very important | Very important | Important | Important | Not mentioned | Important | Not mentioned | Not mentioned | Not mentioned | Important | Not mentioned | Important | Not mentioned | Mentioned | Not mentioned | Mentioned | Not mentioned | Mentioned | Not mentioned | Mentioned | Not mentioned | Not mentioned | Agree strongly | Agree strongly | Disagree | Disagree | Agree | Agree strongly | Agree strongly | Agree | Agree strongly | Agree | Neither agree nor disagree | Neither agree nor disagree | Neither agree nor disagree | Agree | Disagree | Disagree | Neither agree nor disagree | The entire way our society is organized must b... | Bad thing | Don't mind | Don't mind | Not very happy | Good | 7 | 7 | 4 | Sometimes | Rarely | Sometimes | Never | Never | Or about the same | Need to be very careful | Trust completely | Do not trust very much | Trust somewhat | Do not trust very much | Do not trust very much | Do not trust very much | Quite a lot | Quite a lot | Not very much | Not very much | Not very much | Not very much | Not very much | Not very much | Not very much | None at all | Not very much | None at all | None at all | Not very much | Not very much | Not very much | Not very much | Not very much | Not very much | Not very much | Not very much | Not very much | Not very much | Not very much | Not very much | Not very much | Not very much | Not very much | Not very much | 5 | China | Washington DC | Destruction of historic monuments | Inactive member | Active member | Inactive member | Inactive member | Inactive member | Inactive member | Inactive member | Inactive member | Inactive member | Inactive member | Inactive member | Don't belong | 6 | 5 | 3 | Competition is good | 5 | Protecting environment | 8 | Most of them | Most of them | Most of them | Most of them | Most of them | Frequently | Strongly disagree | Very high risk | Neither good, nor bad | Agree | Hard to say | Hard to say | Hard to say | Disagree | Agree | Disagree | Hard to say | Let people come as long as there are jobs avai... | Very Secure | Quite frequently | Quite frequently | Not at all frequently | Quite frequently | Quite frequently | Quite frequently | Very Frequently | Yes | No | No | A great deal | Very much | No | No | Very much | Very much | Very much | Equality | Security | No | A high level of economic growth | Making sure this country has strong defence fo... | Maintaining order in the nation | Fighting rising prices | A stable economy | Progress toward a less impersonal and more hum... | 8 | Completely agree | 7 | 6 | Completely disagree | 8 | Very important | Yes | Yes | Yes | Yes | Strongly agree | Strongly agree | More than once a week | Several times a day | Not a religious person | Follow religious norms and ceremonies | Make sense of life after death | 7 | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | 3 | Never justifiable | 4 | Never justifiable | 8 | 2 | Never justifiable | 4 | 5 | Always justifiable | Definitely should have the right | Definitely should not have the right | Definitely should not have the right | Not at all interested | Never | Never | Never | Never | Never | Never | Daily | Daily | Monthly | Would never do | Would never do | Would never do | Would never do | Would never do | Would never do | Would never do | Would never do | Might do | Would never do | Would never do | Would never do | Never | Never | None | 4.0 | 4.0 | Very often | Fairly often | Not at all often | Fairly often | Fairly often | Very often | Fairly often | Not often | Fairly often | Very often | Not very important | Very little | Very bad | Fairly bad | Very bad | Very good | Very good | 7 | Not an essential characteristic of democracy | 3 | 5 | 9 | 6 | An essential characteristic of democracy | An essential characteristic of democracy | 3 | 9 | 8 | 5 | 3 | Not much respect | Not very proud | Close | Close | Not very close | Not very close | Not close at all | Male | 1999.0 | 22.0 | 16-24 | 16-29 years | I am born in this country | Not an immigrant | Not an immigrant | Morocco | Morocco | Morocco | Yes | 5 | No | Arabic | Single | No children | Primary education (ISCED 1) | Lower | NaN | NaN | Upper secondary education (ISCED 3) | Middle | Lower secondary education (ISCED 2) | Lower | Full time (30 hours a week or more) | NaN | Service (for example: restaurant owner, police... | NaN | Farm worker (for example: farm labourer, tract... | Private non-profit organization | No | Spent some savings | Working class | Seventh step | Medium | Muslim | Islam; nfd | MA: Arabe | Agree | Disagree | Agree strongly | Agree strongly | Disagree | Disagree strongly | Disagree | Disagree | Agree | Agree | Disagree | Agree | Disagree | Disagree | Agree strongly | Disagree | Disagree | Disagree | Agree strongly | Agree strongly | Disagree | Disagree | Agree | Agree strongly | Disagree strongly | Disagree strongly | Agree strongly | Agree strongly | Disagree strongly | Agree strongly | Agree strongly | Agree | Agree strongly | 2 | 1 | 1 | 1 | Materialist | Determination, perseverance/Independence | 0.317500 | 0.360000 | 0.276667 | 0.220000 | Medium | High | Very low | 0.386667 | Very low | Not religious or atheist | 0.000000 | 0.333333 | Conformist | Conformist | Conformist | 0.000000 | High | Low | Low | 0.55 | Yes | No | Yes | 0.666667 | Low | High | High | 0.440000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | Very high | Bajo | 0.000 | 0.000 | 1.0 | Complete | Complete | Complete | Complete | Complete | Complete | Complete | Complete | 1.0 | 1.0 | 1.0 | 0 | 0 | 0 | 0 | 1 |
# searching for anything that contains the letters that are in Satisfaction
variable_view[variable_view['Label'].str.contains('Sati')]
| Variable Name | Label | Values | |
|---|---|---|---|
| 82 | Q49 | Satisfaction with your life | {-5.0: 'Other missing; Multiple answers Mail (... |
| 83 | Q50 | Satisfaction with financial situation of house... | {-5.0: 'Missing; Unknown', -4.0: 'Not asked', ... |
| 291 | Q252 | Satisfaction with the political system perform... | {-5.0: 'Other missing; Multiple answers Mail (... |
# searching for anything that contains the letters that are in Happiness
variable_view[variable_view['Label'].str.contains('hap')]
| Variable Name | Label | Values | |
|---|---|---|---|
| 79 | Q46 | Feeling of happiness | {-5.0: 'Other missing; Multiple answers Mail (... |
# combining the results that we want
DataFrame = pd.DataFrame({
'Satisfaction with financial situation of household (NV_Q50)': df['Q50'],
'Feeling of happiness (NV_Q46)': df['Q46'],
'Feeling of happiness (LV_Q46)': labeled_df['Q46'],
})
DataFrame
| Satisfaction with financial situation of household (NV_Q50) | Feeling of happiness (NV_Q46) | Feeling of happiness (LV_Q46) | |
|---|---|---|---|
| 0 | 6.0 | 2.0 | Quite happy |
| 1 | 2.0 | 4.0 | Not at all happy |
| 2 | 3.0 | 3.0 | Not very happy |
| 3 | 6.0 | 2.0 | Quite happy |
| 4 | 4.0 | 3.0 | Not very happy |
| ... | ... | ... | ... |
| 1195 | 6.0 | 1.0 | Very happy |
| 1196 | 6.0 | 2.0 | Quite happy |
| 1197 | 3.0 | 2.0 | Quite happy |
| 1198 | 3.0 | 2.0 | Quite happy |
| 1199 | 7.0 | 2.0 | Quite happy |
1200 rows × 3 columns
# setting up the frequency table for Feeling of happiness
FoH_frequency_df = DataFrame[['Feeling of happiness (LV_Q46)','Feeling of happiness (NV_Q46)']].value_counts().reset_index().sort_values(by='Feeling of happiness (NV_Q46)')
# calculating the percentages of Feeling of happiness
perFoH = DataFrame[['Feeling of happiness (LV_Q46)']].value_counts(normalize=True)*100
# merge Percentage of Feeling of happiness
FoH_frequency_df = FoH_frequency_df.merge(perFoH, on='Feeling of happiness (LV_Q46)')
#rename the proportion to perFoH
FoH_frequency_df.rename(columns={'proportion':'perFoH'}, inplace=True)
#calculating the mean
mean_SFSH=DataFrame.groupby('Feeling of happiness (LV_Q46)')['Satisfaction with financial situation of household (NV_Q50)'].mean().reset_index().sort_values(by= 'Feeling of happiness (LV_Q46)', ascending=False)
# mearging the mean of Satisfaction with financial situation of household with Feeling of happiness frequency table
FoH_frequency_df=FoH_frequency_df.merge(mean_SFSH, how='left', on='Feeling of happiness (LV_Q46)')
FoH_frequency_df.rename(columns={'Satisfaction with financial situation of household (NV_Q50)':'mean_SFSH'}, inplace=True)
FoH_frequency_df
| Feeling of happiness (LV_Q46) | Feeling of happiness (NV_Q46) | count | perFoH | mean_SFSH | |
|---|---|---|---|---|---|
| 0 | Very happy | 1.0 | 149 | 12.416667 | 7.677852 |
| 1 | Quite happy | 2.0 | 845 | 70.416667 | 6.431953 |
| 2 | Not very happy | 3.0 | 195 | 16.250000 | 4.492308 |
| 3 | Not at all happy | 4.0 | 11 | 0.916667 | 2.818182 |
# calculate the mode and median
Mode_FOH = DataFrame['Feeling of happiness (NV_Q46)'].mode()[0] # Get the first mode
Median_FOH = DataFrame['Feeling of happiness (NV_Q46)'].median()
# assign the mode and median to the columns (handling missing values)
FoH_frequency_df['Mode_FOH'] = Mode_FOH
FoH_frequency_df['Median_FOH'] = Median_FOH
# fill any NaN values if needed
FoH_frequency_df['Mode_FOH'].fillna(Mode_FOH, inplace=True)
FoH_frequency_df['Median_FOH'].fillna(Median_FOH, inplace=True)
order = ['Feeling of happiness (LV_Q46)','Feeling of happiness (NV_Q46)','perFoH','Median_FOH','Mode_FOH','count','mean_SFSH']
FoH_frequency_df[order]
| Feeling of happiness (LV_Q46) | Feeling of happiness (NV_Q46) | perFoH | Median_FOH | Mode_FOH | count | mean_SFSH | |
|---|---|---|---|---|---|---|---|
| 0 | Very happy | 1.0 | 12.416667 | 2.0 | 2.0 | 149 | 7.677852 |
| 1 | Quite happy | 2.0 | 70.416667 | 2.0 | 2.0 | 845 | 6.431953 |
| 2 | Not very happy | 3.0 | 16.250000 | 2.0 | 2.0 | 195 | 4.492308 |
| 3 | Not at all happy | 4.0 | 0.916667 | 2.0 | 2.0 | 11 | 2.818182 |
Mode_FEC = (
DataFrame.groupby('Feeling of happiness (LV_Q46)')['Feeling of happiness (NV_Q46)']
.agg(lambda x: x.mode())
.reset_index()
.sort_values(by='Feeling of happiness (LV_Q46)', ascending=False)
.rename(columns={'Feeling of happiness (NV_Q46)': 'Mode_FEC'})
)
Median_FEC = (
DataFrame.groupby('Feeling of happiness (LV_Q46)')['Feeling of happiness (NV_Q46)']
.agg(lambda x: x.median())
.reset_index()
.sort_values(by='Feeling of happiness (LV_Q46)', ascending=False)
.rename(columns={'Feeling of happiness (NV_Q46)': 'Median_FEC'})
)
FoH_frequency_df=FoH_frequency_df.merge(Mode_FEC, how='left',on='Feeling of happiness (LV_Q46)')
FoH_frequency_df=FoH_frequency_df.merge(Median_FEC, how='left',on='Feeling of happiness (LV_Q46)')
FoH_frequency_df
| Feeling of happiness (LV_Q46) | Feeling of happiness (NV_Q46) | count | perFoH | mean_SFSH | Mode_FOH | Median_FOH | Mode_FEC | Median_FEC | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | Very happy | 1.0 | 149 | 12.416667 | 7.677852 | 2.0 | 2.0 | 1.0 | 1.0 |
| 1 | Quite happy | 2.0 | 845 | 70.416667 | 6.431953 | 2.0 | 2.0 | 2.0 | 2.0 |
| 2 | Not very happy | 3.0 | 195 | 16.250000 | 4.492308 | 2.0 | 2.0 | 3.0 | 3.0 |
| 3 | Not at all happy | 4.0 | 11 | 0.916667 | 2.818182 | 2.0 | 2.0 | 4.0 | 4.0 |
# Create a violing plot
fig = px.violin(
DataFrame,
x='Feeling of happiness (LV_Q46)',
y='Satisfaction with financial situation of household (NV_Q50)',
box=True, # Add boxplot overlay
points='all', # Show all data points
title="The value of Satisfaction with financial situation of household based on Feeling of happiness",
hover_data=['Satisfaction with financial situation of household (NV_Q50)',
'Feeling of happiness (LV_Q46)'] # Cleaner hover data specification
)
# Update the order of categories
fig.update_layout(
xaxis=dict(
categoryorder='array',
categoryarray=['Not at all happy', 'Not very happy', 'Quite happy', 'Very happy']
),
plot_bgcolor='rgba(240, 240, 240, 1)', # Light background color
title_font=dict(size=24, family='Arial', color='black'), # Title font styling
xaxis_title='Feeling of happiness',
yaxis_title='Satisfaction with financial situation of household',
legend=dict(title='Category', orientation='h', x=0.5, xanchor='center'),
)
# Update the violins for styling
fig.update_traces(
width=0.5, # Thinner violins
line_color='black', # Black outline
meanline_visible=True, # Show mean line
scalemode='count', # Adjust width by the count of observations
fillcolor='rgba(0,0,0,0)', # Remove fill
)
# Show the plot
fig.show()
#pio.write_html(fig=fig, auto_open='True' ,file='xdplot.html')
DataFrame[['Feeling of happiness (NV_Q46)']].corrwith(DataFrame['Satisfaction with financial situation of household (NV_Q50)']).reset_index(name='Satisfaction with financial situation of household')
| index | Satisfaction with financial situation of household | |
|---|---|---|
| 0 | Feeling of happiness (NV_Q46) | -0.438542 |
Fixing the "Feeling of Happiness" Scale¶
When I calculated the correlation between Feeling of happiness (LV_Q46) and Satisfaction with financial situation of household (NV_Q50), I got a negative result, which didn’t make sense. After checking the data, I realized that the scale for Feeling of happiness (LV_Q46) was flipped meaning higher numbers showed less happiness, and lower numbers showed more happiness.
To fix this, I created a function to reverse the scale, so that higher numbers now represent more happiness. The new scale works like this:
- 1 → 4
- 2 → 3
- 3 → 2
- 4 → 1
which will be:
- 4: Very happy
- 3: Quite happy
- 2: Not very happy
- 1: Not at all happy
I applied this change and added a new column called Feeling of happiness (NV_Q46)_reversed to the dataset. Now, the values are in the correct order for further analysis.
Here’s the function I used to reverse the scale:
# mapping the old scale of Feeling o happiness with new scale
def reverse_happiness(value):
if pd.notnull(value): # Ensure the value is not NaN
mapping = {1: 4, 2: 3, 3: 2, 4: 1}
return mapping.get(value, value) # Return mapped value or the value itself if not in mapping
return value
DataFrame['Feeling of happiness (NV_Q46)_reversed'] = DataFrame['Feeling of happiness (NV_Q46)'].apply(reverse_happiness)
DataFrame
| Satisfaction with financial situation of household (NV_Q50) | Feeling of happiness (NV_Q46) | Feeling of happiness (LV_Q46) | Feeling of happiness (NV_Q46)_reversed | |
|---|---|---|---|---|
| 0 | 6.0 | 2.0 | Quite happy | 3 |
| 1 | 2.0 | 4.0 | Not at all happy | 1 |
| 2 | 3.0 | 3.0 | Not very happy | 2 |
| 3 | 6.0 | 2.0 | Quite happy | 3 |
| 4 | 4.0 | 3.0 | Not very happy | 2 |
| ... | ... | ... | ... | ... |
| 1195 | 6.0 | 1.0 | Very happy | 4 |
| 1196 | 6.0 | 2.0 | Quite happy | 3 |
| 1197 | 3.0 | 2.0 | Quite happy | 3 |
| 1198 | 3.0 | 2.0 | Quite happy | 3 |
| 1199 | 7.0 | 2.0 | Quite happy | 3 |
1200 rows × 4 columns
DataFrame[['Feeling of happiness (NV_Q46)_reversed']].corrwith(DataFrame['Satisfaction with financial situation of household (NV_Q50)']).reset_index(name='Satisfaction with financial situation of household')
| index | Satisfaction with financial situation of household | |
|---|---|---|
| 0 | Feeling of happiness (NV_Q46)_reversed | 0.438542 |
With this fix, the analysis is now based on the correct Feeling of happiness (NV_Q46)_reversed values, so the results should be more meaningful.
What Does a Correlation of 0.44 Mean?¶
A correlation of 0.438542 tells us how Feeling of happiness and Satisfaction with financial situation are related. In simple terms:
Positive Correlation: The positive number means that when people feel more satisfied with their finances, they tend to feel happier too. So, if someone's financial situation improves, their happiness often increases as well.
Strength of the Relationship: A correlation of 0.44 is considered a moderate relationship. It's not super strong (like 0.9), but it's not weak either. It shows there's a noticeable link, but it’s not the only thing that matters.
What This Means: This result tells us that financial satisfaction and happiness are connected, but it's not the whole story. While improving financial satisfaction can make people happier, other things like health, relationships, or personal goals might also be affecting how happy they feel.
Regression and Why I Use Ordinal Regression¶
Regression is a statistical method used to understand the relationship between a dependent variable and one or more independent variables. It helps in predicting the value of the dependent variable based on the independent variables in other words, Regression is a method used to understand how different factors (independent variables) affect something we want to predict (dependent variable). It helps us estimate the value of one thing based on others.
Why Use Ordinal Regression¶
We use ordinal regression when we want to predict outcomes that have ordered categories. For example, when predicting levels of happiness, where the categories have a clear order:
- 1: Not at all happy
- 2: Not very happy
- 3: Quite happy
- 4: Very happy
Ordinal regression helps us understand the relationship between independent variables and these ordered categories, while respecting the order of the categories. Unlike other methods, it accounts for the fact that the difference between categories might not be the same (for example, the difference between "Not at all happy" and "Not very happy" might not be the same as between "Quite happy" and "Very happy").
The categories have a natural order, but the distance between them is not necessarily equal. In such cases, ordinal regression is the best choice.
Why Not Linear or Logistic Regression?¶
Linear Regression assumes the dependent variable is continuous (like height or income) and has equal spacing between values. This is not suitable for ordinal data where the differences between categories may not be equal.
Logistic Regression is used for binary outcomes (yes/no) or for nominal data (multiple categories without order). It doesn’t account for the order of categories, so it’s not appropriate for ordinal data.
# Independent and dependent variables
X = DataFrame[['Satisfaction with financial situation of household (NV_Q50)']] # Independent variable
y = DataFrame['Feeling of happiness (NV_Q46)_reversed'] # Ordinal dependent variable
# Fit the ordered logit model (ordinal regression)
model = OrderedModel(y, X, distr='logit') # 'logit' specifies the logit link function
result = model.fit()
# Print the model summary
print(result.summary())
# Predict probabilities for each category
predictions = result.predict(X)
# Convert predictions to a NumPy array
predictions = predictions.to_numpy()
# Create a DataFrame to hold the predicted probabilities for each category
predictions_df = pd.DataFrame(predictions, columns=[f'Prob of Category {i+1}' for i in range(predictions.shape[1])])
# Add the satisfaction variable (X) to the DataFrame for plotting
predictions_df['Satisfaction with financial situation'] = DataFrame['Satisfaction with financial situation of household (NV_Q50)']
# Convert the DataFrame into long format for Plotly Express
predictions_long_df = predictions_df.melt(id_vars=['Satisfaction with financial situation'],
value_vars=[f'Prob of Category {i+1}' for i in range(predictions.shape[1])],
var_name='Category',
value_name='Probability')
# Map the happiness categories to human-readable labels
category_mapping = {0: 'Not at all happy',
1: 'Not very happy',
2: 'Quite happy',
3: 'Very happy'}
# Extract category number from the 'Category' column and map to human-readable labels
predictions_long_df['Happiness Category'] = predictions_long_df['Category'].apply(
lambda x: category_mapping[int(x.split()[-1])-1]) # map based on the category index (e.g. Category 1 -> Not at all happy)
# Plot the predicted probabilities using Plotly Express
fig = px.scatter(predictions_long_df,
x='Satisfaction with financial situation',
y='Probability',
color='Happiness Category', # This column contains the mapped labels
title='Ordinal Regression: Predicted Probabilities of Happiness based on Satisfaction with Financial Situation',
labels={'Satisfaction with financial situation': 'Satisfaction with Financial Situation',
'Probability': 'Predicted Probability',
'Happiness Category': 'Happiness Level'})
# Update legend title
fig.update_layout(
legend_title='Happiness Levels' # This is the new title for the legend
)
# Show the plot
fig.show()
Optimization terminated successfully.
Current function value: 0.740002
Iterations: 162
Function evaluations: 277
OrderedModel Results
==================================================================================================
Dep. Variable: Feeling of happiness (NV_Q46)_reversed Log-Likelihood: -888.00
Model: OrderedModel AIC: 1784.
Method: Maximum Likelihood BIC: 1804.
Date: Sat, 29 Mar 2025
Time: 16:23:41
No. Observations: 1200
Df Residuals: 1196
Df Model: 1
===============================================================================================================================
coef std err z P>|z| [0.025 0.975]
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Satisfaction with financial situation of household (NV_Q50) 0.5065 0.034 14.780 0.000 0.439 0.574
1/2 -2.0937 0.340 -6.157 0.000 -2.760 -1.427
2/3 1.2139 0.090 13.491 0.000 1.038 1.390
3/4 1.4424 0.033 43.198 0.000 1.377 1.508
===============================================================================================================================
Interpretation of the Ordinal Regression Results¶
What Was Tested¶
Outcome Variable: "Feeling of happiness (NV_Q46)_reversed"
This is an ordered variable. The term “reversed” suggests that the coding was flipped from the original so that the direction of the scale is switched.Predictor: "Satisfaction with financial situation of household (NV_Q50)"
This variable measures how satisfied people are with their household’s financial situation.
Key Results¶
Satisfaction with Financial Situation (NV_Q50):
- Coefficient: 0.5065
A positive coefficient means that as satisfaction increases, the odds of moving to a higher category on the reversed happiness scale also increase. - Statistical Significance:
The very high z-value (14.780) and p-value (0.000) indicate that this relationship is very unlikely to be due to chance. - Interpretation in Odds:
If you exponentiate the coefficient (e^0.5065 ≈ 1.66), it tells us that for each one-unit increase in satisfaction, the odds of being in a higher happiness category increase by about 66%.
- Coefficient: 0.5065
Thresholds (Cut-off Points Between Categories):
- These values (at -2.0937, 1.2139, and 1.4424) are not predictors but markers that separate the different categories of the happiness variable.
- They help the model decide where one category ends and the next begins on the latent (hidden) scale that the model estimates.
Overall Interpretation¶
- Main Finding:
There is a strong and significant association between satisfaction with the household’s financial situation and the reported feeling of happiness (after reversing the scale). - Practical Meaning:
As people report being more satisfied with their finances, they are more likely to move into a different category of the reversed happiness measure. What “higher category” means depends on how the reversed scale is set up—if a higher number means less happiness, then increased satisfaction could be linked with lower happiness. Conversely, if it was reversed to make higher values mean more happiness, the interpretation would flip. The key is that there is a strong link between financial satisfaction and the way happiness is categorized.
Each of the coefficients and thresholds in the table is statistically significant, which means we can be quite confident in these findings based on the sample of 1200 observations.
Exploring Regional Differences¶
Now that we know financial satisfaction affects happiness, the next step is to check if this relationship changes across different regions of Morocco. By looking at regional patterns, we can see if local factors, like economic conditions or cultural differences, play a role.
In this part, we will use maps and visualizations to explore and compare trends across regions for a clearer understanding.
But before we need to setup the geojson file and dataframe that we will use.
# setupping the map
morocco_data = gpd.read_file("ma.json")
morocco_data.drop(columns='source',inplace=True)
# if the file was in goejson fiormat u need to load t as json format
#morocco_geojson = json.loads(morocco_data.to_json())
morocco_data
| id | name | geometry | |
|---|---|---|---|
| 0 | MA11 | Laâyoune-Sakia El Hamra | POLYGON ((-8.75659 27.14772, -8.75422 27.14715... |
| 1 | MA01 | Tangier-Tetouan-Al Hoceima | POLYGON ((-3.82389 35.19936, -3.82346 35.19887... |
| 2 | MA02 | Oriental | POLYGON ((-3.82012 34.88753, -3.81998 34.89199... |
| 3 | MA08 | Drâa-Tafilalet | POLYGON ((-4.01816 32.60844, -4.00295 32.57946... |
| 4 | MA09 | Souss-Massa | POLYGON ((-7.73179 31.13463, -7.73274 31.13365... |
| 5 | MA10 | Guelmim-Oued Noun | POLYGON ((-8.75659 27.14772, -8.80915 27.16043... |
| 6 | MA06 | Casablanca-Settat | POLYGON ((-9.05932 32.72101, -9.05904 32.7215,... |
| 7 | MA07 | Marrakech-Safi | POLYGON ((-7.18452 31.42896, -7.1929 31.42544,... |
| 8 | MA12 | Dakhla-Oued Ed-Dahab | POLYGON ((-14.90488 24.6832, -14.89861 24.6739... |
| 9 | MA04 | Rabat-Salé-Kenitra | POLYGON ((-6.2434 35.00139, -6.23397 34.99872,... |
| 10 | MA03 | Fez-Meknes | POLYGON ((-5.31515 34.5159, -5.28082 34.51478,... |
| 11 | MA05 | Béni Mellal-Khénifra | POLYGON ((-5.25455 32.86937, -5.25284 32.86344... |
labeled_df['N_REGION_ISO']
0 MA-09 Souss-Massa
1 MA-09 Souss-Massa
2 MA-09 Souss-Massa
3 MA-09 Souss-Massa
4 MA-09 Souss-Massa
...
1195 MA-02 L'Oriental
1196 MA-02 L'Oriental
1197 MA-02 L'Oriental
1198 MA-02 L'Oriental
1199 MA-02 L'Oriental
Name: N_REGION_ISO, Length: 1200, dtype: object
# functoin to seperate the region id from its name
id_list = []
regions_name = []
y = labeled_df['N_REGION_ISO'].str.split(' ')
for x in y:
id_list.append(x[0])
regions_name.append(x[1])
# creating the dataframe the will be ploted 'chromaplot_df'
chromaplot_df = pd.DataFrame({
'Satisfaction with financial situation of household (NV_Q50)': df['Q50'],
'Feeling of happiness (NV_Q46)': df['Q46'],
'Feeling of happiness (NV_Q46)_reversed': DataFrame['Feeling of happiness (NV_Q46)_reversed'],
'Feeling of happiness (LV_Q46)': labeled_df['Q46'],
'N_REGION_ISO': labeled_df['N_REGION_ISO'],
})
chromaplot_df['ID_list'] = id_list
chromaplot_df['ID_list']= chromaplot_df['ID_list'].str.replace('-', '')
chromaplot_df['regions_name'] = regions_name
chromaplot_df
| Satisfaction with financial situation of household (NV_Q50) | Feeling of happiness (NV_Q46) | Feeling of happiness (NV_Q46)_reversed | Feeling of happiness (LV_Q46) | N_REGION_ISO | ID_list | regions_name | |
|---|---|---|---|---|---|---|---|
| 0 | 6.0 | 2.0 | 3 | Quite happy | MA-09 Souss-Massa | MA09 | Souss-Massa |
| 1 | 2.0 | 4.0 | 1 | Not at all happy | MA-09 Souss-Massa | MA09 | Souss-Massa |
| 2 | 3.0 | 3.0 | 2 | Not very happy | MA-09 Souss-Massa | MA09 | Souss-Massa |
| 3 | 6.0 | 2.0 | 3 | Quite happy | MA-09 Souss-Massa | MA09 | Souss-Massa |
| 4 | 4.0 | 3.0 | 2 | Not very happy | MA-09 Souss-Massa | MA09 | Souss-Massa |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 1195 | 6.0 | 1.0 | 4 | Very happy | MA-02 L'Oriental | MA02 | L'Oriental |
| 1196 | 6.0 | 2.0 | 3 | Quite happy | MA-02 L'Oriental | MA02 | L'Oriental |
| 1197 | 3.0 | 2.0 | 3 | Quite happy | MA-02 L'Oriental | MA02 | L'Oriental |
| 1198 | 3.0 | 2.0 | 3 | Quite happy | MA-02 L'Oriental | MA02 | L'Oriental |
| 1199 | 7.0 | 2.0 | 3 | Quite happy | MA-02 L'Oriental | MA02 | L'Oriental |
1200 rows × 7 columns
# calculating the mean of Feeling of happiness for each region
aggregated_data_FoH = chromaplot_df.groupby(['ID_list','N_REGION_ISO'])['Feeling of happiness (NV_Q46)_reversed'].mean().reset_index()
aggregated_data_FoH
| ID_list | N_REGION_ISO | Feeling of happiness (NV_Q46)_reversed | |
|---|---|---|---|
| 0 | MA01 | MA-01 Tanger-Tetouan-Al Hoceima | 2.941667 |
| 1 | MA02 | MA-02 L'Oriental | 3.137500 |
| 2 | MA03 | MA-03 Fes-Meknes | 3.020000 |
| 3 | MA04 | MA-04 Rabat-Sale-Kenitra | 2.911765 |
| 4 | MA05 | MA-05 Beni Mellal-Khenifra | 2.812500 |
| 5 | MA06 | MA-06 Casablanca-Settat | 2.934615 |
| 6 | MA07 | MA-07 Marrakech-Safi | 2.893333 |
| 7 | MA08 | MA-08 Draa-Tafilalet | 2.980000 |
| 8 | MA09 | MA-09 Souss-Massa | 2.940000 |
| 9 | MA10 | MA-10 Guelmim-Oued Noun | 2.900000 |
| 10 | MA11 | MA-11 Laayoune-Sakia El Hamra (EH-partial) | 2.800000 |
| 11 | MA12 | MA-12 Dakhla-Oued Ed-Dahab (EH) | 3.000000 |
# calculating the mean of Satisfaction with financial situation of household for each region
aggregated_data_SFHS = chromaplot_df.groupby(['ID_list','N_REGION_ISO'])['Satisfaction with financial situation of household (NV_Q50)'].mean().reset_index()
aggregated_data_SFHS
| ID_list | N_REGION_ISO | Satisfaction with financial situation of household (NV_Q50) | |
|---|---|---|---|
| 0 | MA01 | MA-01 Tanger-Tetouan-Al Hoceima | 6.508333 |
| 1 | MA02 | MA-02 L'Oriental | 6.262500 |
| 2 | MA03 | MA-03 Fes-Meknes | 6.766667 |
| 3 | MA04 | MA-04 Rabat-Sale-Kenitra | 6.141176 |
| 4 | MA05 | MA-05 Beni Mellal-Khenifra | 5.975000 |
| 5 | MA06 | MA-06 Casablanca-Settat | 6.080769 |
| 6 | MA07 | MA-07 Marrakech-Safi | 6.033333 |
| 7 | MA08 | MA-08 Draa-Tafilalet | 6.340000 |
| 8 | MA09 | MA-09 Souss-Massa | 6.110000 |
| 9 | MA10 | MA-10 Guelmim-Oued Noun | 6.200000 |
| 10 | MA11 | MA-11 Laayoune-Sakia El Hamra (EH-partial) | 6.850000 |
| 11 | MA12 | MA-12 Dakhla-Oued Ed-Dahab (EH) | 5.400000 |
# updating the chromaplot_df
chromaplot_df = chromaplot_df.merge(aggregated_data_FoH, how='left', on='ID_list')
chromaplot_df = chromaplot_df.merge(aggregated_data_SFHS, how='left', on='ID_list')
chromaplot_df.rename(columns={
'Feeling of happiness (NV_Q46)_reversed_x': 'Feeling of happiness (NV_Q46)_reversed',
'Feeling of happiness (NV_Q46)_reversed_y': 'mean_FoH',
'Satisfaction with financial situation of household (NV_Q50)_x':'Satisfaction with financial situation of household (NV_Q50)',
'Satisfaction with financial situation of household (NV_Q50)_y':'mean_SFSH'
},
inplace=True)
chromaplot_df
# re-ordering the placement of the columns
reorder=['ID_list','regions_name','N_REGION_ISO','Feeling of happiness (LV_Q46)','Feeling of happiness (NV_Q46)','Feeling of happiness (NV_Q46)_reversed','mean_FoH','Satisfaction with financial situation of household (NV_Q50)','mean_SFSH']
chromaplot_df = chromaplot_df[reorder]
chromaplot_df
| ID_list | regions_name | N_REGION_ISO | Feeling of happiness (LV_Q46) | Feeling of happiness (NV_Q46) | Feeling of happiness (NV_Q46)_reversed | mean_FoH | Satisfaction with financial situation of household (NV_Q50) | mean_SFSH | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | MA09 | Souss-Massa | MA-09 Souss-Massa | Quite happy | 2.0 | 3 | 2.9400 | 6.0 | 6.1100 |
| 1 | MA09 | Souss-Massa | MA-09 Souss-Massa | Not at all happy | 4.0 | 1 | 2.9400 | 2.0 | 6.1100 |
| 2 | MA09 | Souss-Massa | MA-09 Souss-Massa | Not very happy | 3.0 | 2 | 2.9400 | 3.0 | 6.1100 |
| 3 | MA09 | Souss-Massa | MA-09 Souss-Massa | Quite happy | 2.0 | 3 | 2.9400 | 6.0 | 6.1100 |
| 4 | MA09 | Souss-Massa | MA-09 Souss-Massa | Not very happy | 3.0 | 2 | 2.9400 | 4.0 | 6.1100 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1195 | MA02 | L'Oriental | MA-02 L'Oriental | Very happy | 1.0 | 4 | 3.1375 | 6.0 | 6.2625 |
| 1196 | MA02 | L'Oriental | MA-02 L'Oriental | Quite happy | 2.0 | 3 | 3.1375 | 6.0 | 6.2625 |
| 1197 | MA02 | L'Oriental | MA-02 L'Oriental | Quite happy | 2.0 | 3 | 3.1375 | 3.0 | 6.2625 |
| 1198 | MA02 | L'Oriental | MA-02 L'Oriental | Quite happy | 2.0 | 3 | 3.1375 | 3.0 | 6.2625 |
| 1199 | MA02 | L'Oriental | MA-02 L'Oriental | Quite happy | 2.0 | 3 | 3.1375 | 7.0 | 6.2625 |
1200 rows × 9 columns
# grouping the values by ID_list
chromaplot_df.groupby('ID_list')[['regions_name','N_REGION_ISO','mean_FoH','mean_SFSH']].value_counts().reset_index()
| ID_list | regions_name | N_REGION_ISO | mean_FoH | mean_SFSH | count | |
|---|---|---|---|---|---|---|
| 0 | MA01 | Tanger-Tetouan-Al | MA-01 Tanger-Tetouan-Al Hoceima | 2.941667 | 6.508333 | 120 |
| 1 | MA02 | L'Oriental | MA-02 L'Oriental | 3.137500 | 6.262500 | 80 |
| 2 | MA03 | Fes-Meknes | MA-03 Fes-Meknes | 3.020000 | 6.766667 | 150 |
| 3 | MA04 | Rabat-Sale-Kenitra | MA-04 Rabat-Sale-Kenitra | 2.911765 | 6.141176 | 170 |
| 4 | MA05 | Beni | MA-05 Beni Mellal-Khenifra | 2.812500 | 5.975000 | 80 |
| 5 | MA06 | Casablanca-Settat | MA-06 Casablanca-Settat | 2.934615 | 6.080769 | 260 |
| 6 | MA07 | Marrakech-Safi | MA-07 Marrakech-Safi | 2.893333 | 6.033333 | 150 |
| 7 | MA08 | Draa-Tafilalet | MA-08 Draa-Tafilalet | 2.980000 | 6.340000 | 50 |
| 8 | MA09 | Souss-Massa | MA-09 Souss-Massa | 2.940000 | 6.110000 | 100 |
| 9 | MA10 | Guelmim-Oued | MA-10 Guelmim-Oued Noun | 2.900000 | 6.200000 | 10 |
| 10 | MA11 | Laayoune-Sakia | MA-11 Laayoune-Sakia El Hamra (EH-partial) | 2.800000 | 6.850000 | 20 |
| 11 | MA12 | Dakhla-Oued | MA-12 Dakhla-Oued Ed-Dahab (EH) | 3.000000 | 5.400000 | 10 |
Mean Feeling of happiness (reversed) for every region¶
#creating choropleth plots
fig = px.choropleth_mapbox(
chromaplot_df,
geojson=morocco_data,
locations='ID_list', # Unique region IDs in your DataFrame
featureidkey="properties.id", # Matches IDs in GeoJSON
color='mean_FoH', # Column to visualize
hover_name='N_REGION_ISO', # Column to display region name
hover_data={
'N_REGION_ISO' : True
},
mapbox_style="carto-positron", # Base map style
zoom=4, # Zoom level
center={"lat": 31.7917, "lon": -7.0926}, # Center on Morocco
color_continuous_scale="rdbu", # Color scale for data visualization
title="Average Feeling of Happiness in Morocco", # Map title
range_color=[0, 4]
)
fig.update_layout(
margin={"r":0,"t":30,"l":0,"b":0} # Removes excess margins
)
fig.show()
#pio.write_html(fig, file='plot.html', auto_open=True) # Save and open the plot in a browser
# showing the rating of the regions based on the Mean Feeling of happiness (reversed) for every region for every region
chromaplot_df.groupby('ID_list')[['N_REGION_ISO','mean_FoH','mean_SFSH']].value_counts().reset_index().sort_values(by='mean_FoH', ascending=False)
| ID_list | N_REGION_ISO | mean_FoH | mean_SFSH | count | |
|---|---|---|---|---|---|
| 1 | MA02 | MA-02 L'Oriental | 3.137500 | 6.262500 | 80 |
| 2 | MA03 | MA-03 Fes-Meknes | 3.020000 | 6.766667 | 150 |
| 11 | MA12 | MA-12 Dakhla-Oued Ed-Dahab (EH) | 3.000000 | 5.400000 | 10 |
| 7 | MA08 | MA-08 Draa-Tafilalet | 2.980000 | 6.340000 | 50 |
| 0 | MA01 | MA-01 Tanger-Tetouan-Al Hoceima | 2.941667 | 6.508333 | 120 |
| 8 | MA09 | MA-09 Souss-Massa | 2.940000 | 6.110000 | 100 |
| 5 | MA06 | MA-06 Casablanca-Settat | 2.934615 | 6.080769 | 260 |
| 3 | MA04 | MA-04 Rabat-Sale-Kenitra | 2.911765 | 6.141176 | 170 |
| 9 | MA10 | MA-10 Guelmim-Oued Noun | 2.900000 | 6.200000 | 10 |
| 6 | MA07 | MA-07 Marrakech-Safi | 2.893333 | 6.033333 | 150 |
| 4 | MA05 | MA-05 Beni Mellal-Khenifra | 2.812500 | 5.975000 | 80 |
| 10 | MA11 | MA-11 Laayoune-Sakia El Hamra (EH-partial) | 2.800000 | 6.850000 | 20 |
showing the rating of the regions based on mean of Satisfaction with financial situation of household for every region¶
#creating choropleth plots
fig = px.choropleth_mapbox(
chromaplot_df,
geojson=morocco_data,
locations='ID_list', # Unique region IDs in your DataFrame
featureidkey="properties.id", # Matches IDs in GeoJSON
color='mean_SFSH', # Column to visualize
hover_name='N_REGION_ISO', # Column to display region name
hover_data={
'N_REGION_ISO' : True,
'mean_FoH': True
},
mapbox_style="carto-positron", # Base map style
zoom=4, # Zoom level
center={"lat": 31.7917, "lon": -7.0926}, # Center on Morocco
color_continuous_scale="deep", # Color scale for data visualization
title="Average Satisfaction with financial situation of household in Morocco",# Map title
range_color=[0,10]
)
fig.update_layout(
margin={"r":0,"t":30,"l":0,"b":0} # Removes excess margins
)
fig.show()
#pio.write_html(fig, file='plot.html', auto_open=True) # Save and open the plot in a browser
# showing the rating of the regions based on the Mean of Satisfaction with financial situation of household for every region
chromaplot_df.groupby('ID_list')[['N_REGION_ISO','mean_FoH','mean_SFSH']].value_counts().reset_index().sort_values(by='mean_SFSH', ascending=False)
| ID_list | N_REGION_ISO | mean_FoH | mean_SFSH | count | |
|---|---|---|---|---|---|
| 10 | MA11 | MA-11 Laayoune-Sakia El Hamra (EH-partial) | 2.800000 | 6.850000 | 20 |
| 2 | MA03 | MA-03 Fes-Meknes | 3.020000 | 6.766667 | 150 |
| 0 | MA01 | MA-01 Tanger-Tetouan-Al Hoceima | 2.941667 | 6.508333 | 120 |
| 7 | MA08 | MA-08 Draa-Tafilalet | 2.980000 | 6.340000 | 50 |
| 1 | MA02 | MA-02 L'Oriental | 3.137500 | 6.262500 | 80 |
| 9 | MA10 | MA-10 Guelmim-Oued Noun | 2.900000 | 6.200000 | 10 |
| 3 | MA04 | MA-04 Rabat-Sale-Kenitra | 2.911765 | 6.141176 | 170 |
| 8 | MA09 | MA-09 Souss-Massa | 2.940000 | 6.110000 | 100 |
| 5 | MA06 | MA-06 Casablanca-Settat | 2.934615 | 6.080769 | 260 |
| 6 | MA07 | MA-07 Marrakech-Safi | 2.893333 | 6.033333 | 150 |
| 4 | MA05 | MA-05 Beni Mellal-Khenifra | 2.812500 | 5.975000 | 80 |
| 11 | MA12 | MA-12 Dakhla-Oued Ed-Dahab (EH) | 3.000000 | 5.400000 | 10 |
Regional Differences in the Relationship Between Financial Satisfaction and Happiness¶
Based on the choropleth map, we observe that the relationship between financial satisfaction and happiness varies across different regions of Morocco. In some areas, such as major cities like Casablanca and Rabat, we see a stronger connection, where higher financial satisfaction aligns with greater levels of happiness. This could be because these urban centers offer better job opportunities, improved infrastructure, and access to resources that support a higher standard of living.
On the other hand, regions like the Atlas Mountains or rural areas in the southern provinces show weaker or less consistent patterns. In these areas, despite financial satisfaction being lower, happiness levels do not always follow the same trend. Factors such as limited economic opportunities, cultural differences, or access to fewer resources might explain these inconsistencies.
These differences show that the connection between financial satisfaction and happiness can change depending on where people live. Understanding these regional factors can help us better understand why happiness and financial satisfaction are linked in some areas but not in others.